Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

High-throughput and proteome-wide discovery of endogenous biomolecular condensates

Abstract

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell’s proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic diagram of high-throughput identification of biomolecular condensates.
Fig. 2: High-throughput identification of phase-separating proteins.
Fig. 3: Validation of condensate proteins identified from volume compression.
Fig. 4: Identification of condensate proteins in response to short-term treatment with TGF-β.
Fig. 5: Identification of condensate proteins in response to long-term treatment with TGF-β.
Fig. 6: Proteome-wide analysis of endogenous-expression phase-separating proteins.

Similar content being viewed by others

Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE114 partner repository with the dataset identifier PXD048218. Due to the size and lack of available condensate imaging databases, raw imaging data are available upon request to the corresponding author. All primary data are included in the source data associated with each figure accompanying this paper. Source data are provided with this paper.

References

  1. Lyon, A. S., Peeples, W. B. & Rosen, M. K. A framework for understanding the functions of biomolecular condensates across scales. Nat. Rev. Mol. Cell Biol. 22, 215–235 (2021).

    CAS  PubMed  Google Scholar 

  2. Alberti, S. & Hyman, A. A. Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing. Nat. Rev. Mol. Cell Biol. 22, 196–213 (2021).

    CAS  PubMed  Google Scholar 

  3. Delarue, M. et al. mTORC1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding. Cell 174, 338–349 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Li, Y., Tang, W. & Guo, M. The cell as matter: connecting molecular biology to cellular functions. Matter 4, 1863–1891 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Walter, H. & Brooks, D. E. Phase separation in cytoplasm, due to macromolecular crowding, is the basis for microcompartmentation. FEBS Lett. 361, 135–139 (1995).

    CAS  PubMed  Google Scholar 

  6. Fujioka, Y. et al. Phase separation organizes the site of autophagosome formation. Nature 578, 301–305 (2020).

    CAS  PubMed  Google Scholar 

  7. Yasuda, S. et al. Stress- and ubiquitylation-dependent phase separation of the proteasome. Nature 578, 296–300 (2020).

    CAS  PubMed  Google Scholar 

  8. Wei, M.-T. et al. Phase behaviour of disordered proteins underlying low density and high permeability of liquid organelles. Nat. Chem. 9, 1118–1125 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Frottin, F. et al. The nucleolus functions as a phase-separated protein quality control compartment. Science 365, 342–347 (2019).

    CAS  PubMed  Google Scholar 

  10. Klosin, A. et al. Phase separation provides a mechanism to reduce noise in cells. Science 367, 464–468 (2020).

    CAS  PubMed  Google Scholar 

  11. Liu, Q. et al. Glycogen accumulation and phase separation drives liver tumor initiation. Cell 184, 5559–5576 (2021).

    CAS  PubMed  Google Scholar 

  12. Mathieu, C., Pappu, R. V. & Taylor, J. P. Beyond aggregation: pathological phase transitions in neurodegenerative disease. Science 370, 56–60 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Michaels, T. C. et al. Dynamics of oligomer populations formed during the aggregation of Alzheimer’s Aβ42 peptide. Nat. Chem. 12, 445–451 (2020).

    CAS  PubMed  Google Scholar 

  14. Bremer, A. et al. Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains. Nat. Chem. 14, 196–207 (2022).

    CAS  PubMed  Google Scholar 

  15. Lafontaine, D. L., Riback, J. A., Bascetin, R. & Brangwynne, C. P. The nucleolus as a multiphase liquid condensate. Nat. Rev. Mol. Cell Biol. 22, 165–182 (2021).

    CAS  PubMed  Google Scholar 

  16. Lee, D. S., Wingreen, N. S. & Brangwynne, C. P. Chromatin mechanics dictates subdiffusion and coarsening dynamics of embedded condensates. Nat. Phys. 17, 531–538 (2021).

    CAS  Google Scholar 

  17. Riback, J. A. et al. Composition-dependent thermodynamics of intracellular phase separation. Nature 581, 209–214 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Shimobayashi, S. F., Ronceray, P., Sanders, D. W., Haataja, M. P. & Brangwynne, C. P. Nucleation landscape of biomolecular condensates. Nature 599, 503–506 (2021).

    CAS  PubMed  Google Scholar 

  19. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    PubMed  Google Scholar 

  20. Garaizar, A. et al. Aging can transform single-component protein condensates into multiphase architectures. Proc. Natl Acad. Sci. USA 119, e2119800119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Krainer, G. et al. Reentrant liquid condensate phase of proteins is stabilized by hydrophobic and non-ionic interactions. Nat. Commun. 12, 1085 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Shen, Y. et al. Biomolecular condensates undergo a generic shear-mediated liquid-to-solid transition. Nat. Nanotechnol. 15, 841–847 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Toprakcioglu, Z. et al. Adsorption free energy predicts amyloid protein nucleation rates. Proc. Natl Acad. Sci. USA 119, e2109718119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Choi, S., Meyer, M. O., Bevilacqua, P. C. & Keating, C. D. Phase-specific RNA accumulation and duplex thermodynamics in multiphase coacervate models for membraneless organelles. Nat. Chem. 14, 1110–1117 (2022).

  25. Dzuricky, M., Rogers, B. A., Shahid, A., Cremer, P. S. & Chilkoti, A. De novo engineering of intracellular condensates using artificial disordered proteins. Nat. Chem. 12, 814–825 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lv, P. et al. O-GlcNAcylation modulates liquid–liquid phase separation of SynGAP/PSD-95. Nat. Chem. 14, 831–840 (2022).

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

    CAS  PubMed  Google Scholar 

  28. Hardenberg, M., Horvath, A., Ambrus, V., Fuxreiter, M. & Vendruscolo, M. Widespread occurrence of the droplet state of proteins in the human proteome. Proc. Natl Acad. Sci. USA 117, 33254–33262 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Saar, K. L. et al. Learning the molecular grammar of protein condensates from sequence determinants and embeddings. Proc. Natl Acad. Sci. USA 118, e2019053118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176, 419–434 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Li, P. et al. Phase transitions in the assembly of multivalent signalling proteins. Nature 483, 336–340 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Franzmann, T. M. & Alberti, S. Prion-like low-complexity sequences: key regulators of protein solubility and phase behavior. J. Biol. Chem. 294, 7128–7136 (2019).

    CAS  PubMed  Google Scholar 

  34. Dong, R.-Y. & Granick, S. Reincarnations of the phase separation problem. Nat. Commun. 12, 911 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Han, T. W. et al. Cell-free formation of RNA granules: bound RNAs identify features and components of cellular assemblies. Cell 149, 768–779 (2012).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Sanchez de Groot, N. et al. RNA structure drives interaction with proteins. Nat. Commun. 10, 3246 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. Shi, M. et al. Quantifying the phase separation property of chromatin-associated proteins under physiological conditions using an anti-1,6-hexanediol index. Genome Biol. 22, 229 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Jalihal, A. P. et al. Multivalent proteins rapidly and reversibly phase-separate upon osmotic cell volume change. Mol. Cell 79, 978–990 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Esposito, M. et al. TGF-β-induced DACT1 biomolecular condensates repress Wnt signalling to promote bone metastasis. Nat. Cell Biol. 23, 257–267 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Geladaki, A. et al. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun. 10, 331 (2019).

    PubMed  PubMed Central  Google Scholar 

  43. Mitrea, D. M. et al. Methods for physical characterization of phase-separated bodies and membrane-less organelles. J. Mol. Biol. 430, 4773–4805 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhou, M. et al. Phase-separated condensate-aided enrichment of biomolecular interactions for high-throughput drug screening in test tubes. J. Biol. Chem. 295, 11420–11434 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Caudron-Herger, M. et al. R-DeeP: proteome-wide and quantitative identification of RNA-dependent proteins by density gradient ultracentrifugation. Mol. Cell 75, 184–199 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sridharan, S. et al. Systematic discovery of biomolecular condensate-specific protein phosphorylation. Nat. Chem. Biol. 18, 1104–1114 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Hernández, A. R., Klein, A. M. & Kirschner, M. W. Kinetic responses of β-catenin specify the sites of Wnt control. Science 338, 1337–1340 (2012).

    PubMed  Google Scholar 

  48. Kim, S.-E. et al. Wnt stabilization of β-catenin reveals principles for morphogen receptor-scaffold assemblies. Science 340, 867–870 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Ma, W. et al. Single-molecule dynamics of dishevelled at the plasma membrane and Wnt pathway activation. Proc. Natl Acad. Sci. USA 117, 16690–16701 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Sasagawa, S., Ozaki, Y.-i, Fujita, K. & Kuroda, S. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat. Cell Biol. 7, 365–373 (2005).

    CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  52. Elbaum-Garfinkle, S. et al. The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics. Proc. Natl Acad. Sci. USA 112, 7189–7194 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Reichheld, S. E., Muiznieks, L. D., Keeley, F. W. & Sharpe, S. Direct observation of structure and dynamics during phase separation of an elastomeric protein. Proc. Natl Acad. Sci. USA 114, E4408–E4415 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Ribeiro, S. S., Samanta, N., Ebbinghaus, S. & Marcos, J. C. The synergic effect of water and biomolecules in intracellular phase separation. Nat. Rev. Chem. 3, 552–561 (2019).

    CAS  Google Scholar 

  55. Stradner, A. et al. Equilibrium cluster formation in concentrated protein solutions and colloids. Nature 432, 492–495 (2004).

    CAS  PubMed  Google Scholar 

  56. Bracha, D., Walls, M. T. & Brangwynne, C. P. Probing and engineering liquid-phase organelles. Nat. Biotechnol. 37, 1435–1445 (2019).

    CAS  PubMed  Google Scholar 

  57. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Davis, R. B., Moosa, M. M. & Banerjee, P. R. Ectopic biomolecular phase transitions: fusion proteins in cancer pathologies. Trends Cell Biol. 32, 681–695 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Tiwary, A. K. & Zheng, Y. Protein phase separation in mitosis. Curr. Opin. Cell Biol. 60, 92–98 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Bracha, D. et al. Mapping local and global liquid phase behavior in living cells using photo-oligomerizable seeds. Cell 175, 1467–1480 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Cai, D. et al. Phase separation of YAP reorganizes genome topology for long-term YAP target gene expression. Nat. Cell Biol. 21, 1578–1589 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, Y. et al. Volumetric compression induces intracellular crowding to control intestinal organoid growth via Wnt/β-catenin signaling. Cell Stem Cell 28, 63–78 (2021).

    CAS  PubMed  Google Scholar 

  63. Cox, B. & Emili, A. Tissue subcellular fractionation and protein extraction for use in mass-spectrometry-based proteomics. Nat. Protoc. 1, 1872–1878 (2006).

    CAS  PubMed  Google Scholar 

  64. Money, N. P. Osmotic pressure of aqueous polyethylene glycols: relationship between molecular weight and vapor pressure deficit. Plant Physiol. 91, 766–769 (1989).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Qian, D. et al. Tie-line analysis reveals interactions driving heteromolecular condensate formation. Phys. Rev. X 12, 041038 (2022).

    CAS  Google Scholar 

  66. Akabayov, B., Akabayov, S. R., Lee, S.-J., Wagner, G. & Richardson, C. C. Impact of macromolecular crowding on DNA replication. Nat. Commun. 4, 1615 (2013).

    PubMed  Google Scholar 

  67. Alghoul, E., Basbous, J. & Constantinou, A. An optogenetic proximity labeling approach to probe the composition of inducible biomolecular condensates in cultured cells. STAR Protoc. 2, 100677 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. An, H., Ordureau, A., Körner, M., Paulo, J. A. & Harper, J. W. Systematic quantitative analysis of ribosome inventory during nutrient stress. Nature 583, 303–309 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Garcia-Jove Navarro, M. et al. RNA is a critical element for the sizing and the composition of phase-separated RNA–protein condensates. Nat. Commun. 10, 3230 (2019).

    PubMed  PubMed Central  Google Scholar 

  70. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Meszaros, B., Erdos, G. & Dosztanyi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 46, W329–W337 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Hou, C. et al. PhaSepDB in 2022: annotating phase separation-related proteins with droplet states, co-phase separation partners and other experimental information. Nucleic Acids Res. 51, D460–D465 (2023).

    CAS  PubMed  Google Scholar 

  73. Zeng, W. J. et al. Initiation of stress granule assembly by rapid clustering of IGF2BP proteins upon osmotic shock. Biochim. Biophys. Acta Mol. Cell Res. 1867, 118795 (2020).

    CAS  PubMed  Google Scholar 

  74. Jung, J. U., Taylor, C. A. T. & Cobb, M. H. Crank up the volume: osmotic stress induces WNK1 phase separation. Cell Res. 33, 265–266 (2023).

    CAS  PubMed  Google Scholar 

  75. Majumder, S. & Jain, A. Osmotic stress triggers phase separation. Mol. Cell 79, 876–877 (2020).

    CAS  PubMed  Google Scholar 

  76. Kundinger, S. R. et al. Phosphorylation regulates arginine-rich RNA-binding protein solubility and oligomerization. J. Biol. Chem. 297, 101306 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Liu, X.-M., Ma, L. & Schekman, R. Selective sorting of microRNAs into exosomes by phase-separated YBX1 condensates. eLife 10, e71982 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lu, X. et al. Copy number amplification and SP1-activated lncRNA MELTF-AS1 regulates tumorigenesis by driving phase separation of YBX1 to activate ANXA8 in non-small cell lung cancer. Oncogene 41, 3222–3238 (2022).

    CAS  PubMed  Google Scholar 

  79. Guo, M. et al. Cell volume change through water efflux impacts cell stiffness and stem cell fate. Proc. Natl Acad. Sci. USA 114, E8618–E8627 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Li, Y. et al. Helical nanofiber yarn enabling highly stretchable engineered microtissue. Proc. Natl Acad. Sci. USA 116, 9245–9250 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Li, Y. et al. Compression-induced dedifferentiation of adipocytes promotes tumor progression. Sci. Adv. 6, eaax5611 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Bergmann, J. E. & Lodish, H. F. A kinetic model of protein synthesis. Application to hemoglobin synthesis and translational control. J. Biol. Chem. 254, 11927–11937 (1979).

    CAS  PubMed  Google Scholar 

  83. Besse, F. & Ephrussi, A. Translational control of localized mRNAs: restricting protein synthesis in space and time. Nat. Rev. Mol. Cell Biol. 9, 971–980 (2008).

    CAS  PubMed  Google Scholar 

  84. Brittis, P. A., Lu, Q. & Flanagan, J. G. Axonal protein synthesis provides a mechanism for localized regulation at an intermediate target. Cell 110, 223–235 (2002).

    CAS  PubMed  Google Scholar 

  85. Haschemeyer, A. E. Kinetics of protein synthesis in higher organisms in vivo. Trends Biochem. Sci. 1, 133–136 (1976).

    CAS  Google Scholar 

  86. Rannels, D. E., Wartell, S. A. & Watkins, C. A. The measurement of protein synthesis in biological systems. Life Sci. 30, 1679–1690 (1982).

    CAS  PubMed  Google Scholar 

  87. Heinrich, R., Neel, B. G. & Rapoport, T. A. Mathematical models of protein kinase signal transduction. Mol. Cell 9, 957–970 (2002).

    CAS  PubMed  Google Scholar 

  88. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    PubMed  Google Scholar 

  89. Su, Q., Mehta, S. & Zhang, J. Liquid–liquid phase separation: orchestrating cell signaling through time and space. Mol. Cell 81, 4137–4146 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Tripathi, S., Levine, H. & Jolly, M. K. The physics of cellular decision making during epithelial-mesenchymal transition. Annu. Rev. Biophys. 49, 1–18 (2020).

    CAS  PubMed  Google Scholar 

  91. Wong, I. Y. et al. Collective and individual migration following the epithelial–mesenchymal transition. Nat. Mater. 13, 1063–1071 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Zhang, J. et al. TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci. Signal. 7, ra91 (2014).

    PubMed  Google Scholar 

  93. Erdel, F. et al. Mouse heterochromatin adopts digital compaction states without showing hallmarks of HP1-driven liquid–liquid phase separation. Mol. Cell 78, 236–249 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Fu, H. et al. Poly(ADP-ribosylation) of P-TEFb by PARP1 disrupts phase separation to inhibit global transcription after DNA damage. Nat. Cell Biol. 24, 513–525 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Gardell, S. J. et al. Boosting NAD+ with a small molecule that activates NAMPT. Nat. Commun. 10, 3241 (2019).

    PubMed  PubMed Central  Google Scholar 

  96. Attisano, L. & Wrana, J. L. Signal integration in TGF-β, WNT and Hippo pathways. F1000Prime Rep. 5, 17 (2013).

  97. Su, X. et al. Phase separation of signaling molecules promotes T cell receptor signal transduction. Science 352, 595–599 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Zbinden, A., Pérez-Berlanga, M., De Rossi, P. & Polymenidou, M. Phase separation and neurodegenerative diseases: a disturbance in the force. Dev. Cell 55, 45–68 (2020).

    CAS  PubMed  Google Scholar 

  99. Li, Q. et al. LLPSDB: a database of proteins undergoing liquid-liquid phase separation in vitro. Nucleic Acids Res. 48, D320–D327 (2020).

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  101. Guo, M. et al. Probing the stochastic, motor-driven properties of the cytoplasm using force spectrum microscopy. Cell 158, 822–832 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Gupta, S. K. & Guo, M. Equilibrium and out-of-equilibrium mechanics of living mammalian cytoplasm. J. Mech. Phys. Solids 107, 284–293 (2017).

    Google Scholar 

  103. Jawerth, L. et al. Protein condensates as aging Maxwell fluids. Science 370, 1317–1323 (2020).

    CAS  PubMed  Google Scholar 

  104. Spruijt, E., Sokolova, E. & Huck, W. T. Complexity of molecular crowding in cell-free enzymatic reaction networks. Nat. Nanotechnol. 9, 406–407 (2014).

    CAS  PubMed  Google Scholar 

  105. Zhang, Q. et al. Visualizing dynamics of cell signaling in vivo with a phase separation-based kinase reporter. Mol. Cell 69, 334–346 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Kanshin, E., Bergeron-Sandoval, L.-P., Isik, S. S., Thibault, P. & Michnick, S. W. A cell-signaling network temporally resolves specific versus promiscuous phosphorylation. Cell Rep. 10, 1202–1214 (2015).

    CAS  PubMed  Google Scholar 

  107. Chakfe, Y. & Bourque, C. W. Excitatory peptides and osmotic pressure modulate mechanosensitive cation channels in concert. Nat. Neurosci. 3, 572–579 (2000).

    CAS  PubMed  Google Scholar 

  108. Molloy, M. P. Two-dimensional electrophoresis of membrane proteins using immobilized pH gradients. Anal. Biochem. 280, 1–10 (2000).

    CAS  PubMed  Google Scholar 

  109. Rimpilainen, M. A. & Righetti, P. G. Membrane protein analysis by isoelectric focusing in immobilized pH gradients. Electrophoresis 6, 419–422 (1985).

    CAS  Google Scholar 

  110. Zhang, L. et al. Proteomic analysis of mouse liver plasma membrane: use of differential extraction to enrich hydrophobic membrane proteins. Proteomics 5, 4510–4524 (2005).

    CAS  PubMed  Google Scholar 

  111. Consortium, T. U. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2020).

    Google Scholar 

  112. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).

    CAS  PubMed  Google Scholar 

  114. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge financial support from the National Natural Science Foundation of China (grants 32171248 and 12102142 to Y.L., 22074047 and 21775049 to B.-F.L. and 31700746 to P.C.) and the Fundamental Research Funds for Central Universities (HUST no. 2021GCRC056 to Y.L.).

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization was provided by Y.L. and B.-F.L., methodology by Y.L. and P.L., investigations by P.L., P.C., Y.L., H.X., L.L., M.L., X.R., W.W., W.Z., L.Z., X.X., Y.Z. and L.X., visualization by Y.L., P.L., P.C., F.Q., J.S., J.L., P.Z., Z.G., X.F., W.D. and X.L., funding acquisition by Y.L. and B.-F.L., project administration by Y.L. and supervision by Y.L. and B.-F.L. The original draft was written by Y.L. and P.L., and writing, review and editing by Y.L., P.L. and B.-F.L.

Corresponding authors

Correspondence to Bi-Feng Liu or Yiwei Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemistry thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 In situ imaging confirmed that the isolated condensates remained undissociated upon the treatment of the RIPA or NP-40 lysis buffer.

a, Cells were transfected YBX1-EGFP to form YBX1-EGFP condensates in microfluidic channel. The RIPA buffer or NP-40 buffer was introduced into the cells by microchannels. b, YBX1-EGFP condensates remained for at least 40 mins with the treatment of NP-40 buffer. However, NP-40 was observed to insufficiently dissolve cell membrane and be unable to disrupt the nuclear membrane. c, YBX1-EGFP condensates remained for at least 40 mins with the treatment of RIPA buffer, while cellular lipid membrane and nuclear structure were sufficiently dissolved by RIPA buffer. Representative results from three independent experiments.

Extended Data Fig. 2 In situ imaging confirmed that the condensates still retained upon the step of pre-clear centrifugation.

a, Cell lysate was centrifuged by 12000 g, and supernatant was introduced into the cells by microchannels. b, YBX1-EGFP condensates remained after pre-clear centrifugation step. c, YAP1-EGFP condensates remained after pre-clear centrifugation step. Representative results from two independent experiments.

Extended Data Fig. 3 Testing droplet-like behaviors of MRPL23 condensates and TOE1 condensates.

a, Time-dependent images showed the liquid-like fusion and splitting behaviors of the MRPL23-GFP condensates. b, Fluorescent image of MRPL23-GFP condensates. The dash line circle indicated the area of nucleus. c, The percentage of MRPL23-GFP in the condensed form as compared to the total cellular protein. n = 7 independent experiments. d-e, Quantification (d) and sequential images (e) of FRAP assays on MRPL23-GFP condensates showed the recovery of MRPL23-GFP after photo-bleaching. n = 6 independent experiments. f, Time-dependent images showed the liquid-like fusion and splitting behaviors of the TOE1-GFP condensates. g, Fluorescent image of TOE1-GFP condensates. The dash line circle indicated the area of nucleus. h, The percentage of TOE1-GFP in the condensed form as compared to the total cellular protein. n = 10 independent experiments. i-j, Quantification (i) and sequential images (j) of FRAP assays on TOE1-GFP condensates showed the recovery of TOE1-GFP after photo-bleaching. n = 6 independent experiments. In c, d, h, i, data are means ± SD and were analysed by two-sided unpaired student’s t test.

Source data

Extended Data Fig. 4 Testing droplet-like behaviors of ASNS condensates and LTA4H condensates.

a, Time-dependent images showed the liquid-like fusion and splitting behaviors of the ASNS-GFP condensates. b, Fluorescent image of ASNS-GFP condensates. The dash line circle indicated the area of nucleus. c, The percentage of ASNS-GFP in the condensed form as compared to the total cellular protein. n = 7 independent experiments, OE represents overexpression. d-e, Quantification (d) and sequential images (e) of FRAP assays on ASNS-GFP condensates showed the recovery of ASNS-GFP after photo-bleaching. n = 6 independent experiments. f, Time-dependent images showed the liquid-like fusion and splitting behaviors of the LTA4H-GFP condensates. g, Fluorescent image of LTA4H-GFP condensates. The dash line circle indicated the area of nucleus. h, The percentage of LTA4H-GFP in the condensed form as compared to the total cellular protein. n = 7 independent experiments, OE represents overexpression. i-j, Quantification (i) and sequential images (j) of FRAP assays on LTA4H-GFP condensates showed the recovery of LTA4H-GFP after photo-bleaching. n = 6 independent experiments. k, Numbers of detected proteins of 8 types of known membraneless condensates in the detected condensate proteins upon the short-term treatment of TGF-β. In c, d, h, i, data are means ± SD and were analysed by two-sided unpaired student’s t test.

Source data

Extended Data Fig. 5 Testing droplet-like behaviors of DAP3 condensates and IDH1 condensates.

a, Time-dependent images showed the liquid-like fusion and splitting behaviors of the DAP3-GFP condensates. b, Fluorescent image of DAP3-GFP condensates. The dash line circle indicated the area of nucleus. c, The percentage of DAP3-GFP in the condensed form as compared to the total cellular protein. n = 7 independent experiments, OE represents overexpression. d-e, Quantification (d) and sequential images (e) of FRAP assays on DAP3-GFP condensates showed the recovery of ASNS-GFP after photo-bleaching. n = 6 independent experiments. f, Time-dependent images showed the liquid-like fusion and splitting behaviors of the IDH1-GFP condensates. g, Fluorescent image of IDH1-GFP condensates. The dash line circle indicated the area of nucleus. h, The percentage of IDH1-GFP in the condensed form as compared to the total cellular protein. n = 7 independent experiments, OE represents overexpression. i-j, Quantification (i) and sequential images (j) of FRAP assays on IDH1-GFP condensates showed the recovery of IDH1-GFP after photo-bleaching. n = 6 independent experiments. k, Fluorescence images of DAP3-EGFP and IDH1-EGFP transfected into H1975 cells without TGF-β induction (left) and with 2 days of TGF-β treatment (right). Representative results from three independent experiments. l, Numbers of detected proteins of 8 types of known membraneless condensates in the detected condensate proteins upon the long-term treatment of TGF-β. In c, d, h, i, data are means ± SD and were analysed by two-sided unpaired student’s t test.

Source data

Supplementary information

Supplementary Information

Resource table, materials and methods, discussions sections 1–8, Supplementary Figs. 1–30, Tables 1–5, references and raw data of uncropped gel scans.

Reporting Summary

Supplementary Table 1

Results of high-throughput identification of phase separated proteins.

Source data

Source Data Fig. 2

Statistical source data for Figs. 2c, 2d.

Source Data Fig. 3

Statistical source data for Figs. 3e, 3g, 3j, 3k, 3o, 3p.

Source Data Fig. 4

Statistical source data for Figs. 4e, 4g, 4j, 4k, 4o, 4p.

Source Data Fig. 5

Statistical source data for Figs. 5e, 5g, 5j, 5k, 5o, 5p.

Source Data Extended Data Fig. 3

Statistical source data Extended Data Figs. 3c, 3d, 3h, 3i.

Source Data Extended Data Fig. 4

Statistical source data Extended Data Figs. 4c, 4d, 4h, 4i.

Source Data Extended Data Fig. 5

Statistical source data Extended Data Figs. 5c, 5d, 5h, 5i.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, P., Chen, P., Qi, F. et al. High-throughput and proteome-wide discovery of endogenous biomolecular condensates. Nat. Chem. 16, 1101–1112 (2024). https://doi.org/10.1038/s41557-024-01485-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-024-01485-1

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing