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Raman Spectroscopy and FRAP Analysis on Density Heterogeneity and Diffusion Behavior of Complex Coacervation by
Ya Tian1, Qiang Zhang2, Jiangtao Li1
1Research Institute for Electronic Science (RIES) and Division of Information Science and Technology, Hokkaido University, N20W10, Kita ward, Sapporo, Hokkaido 001-0020, Japan.
ACS Omega
|September 15, 2025
Summary
Charge patterning in arginine-rich dipeptides, not just net charge, critically influences liquid-liquid phase separation (LLPS) dynamics and droplet density, impacting cellular function and neurodegenerative diseases.
Area of Science:
- Biochemistry
- Cell Biology
- Biophysics
Background:
- Membraneless organelles (MLOs) form via liquid-liquid phase separation (LLPS), crucial for cellular processes.
- Aberrant LLPS and condensate formation are linked to neurodegenerative diseases.
- Dipeptide repeat proteins like poly-(proline-arginine) (poly-PR) and poly-(glycine-arginine) (poly-GR) from C9ORF72 mutations disrupt cellular homeostasis via LLPS.
Purpose of the Study:
- To investigate how sequence-specific charge distribution, beyond net charge, affects LLPS dynamics.
- To analyze the density heterogeneity and diffusion behavior of LLPS droplets formed by arginine-rich dipeptides and poly-A RNA.
Main Methods:
- Combined fluorescence recovery after photobleaching (FRAP) and Raman spectroscopy.
- Examined LLPS droplets formed by (PR)20, (P4R4)5, and (GR)20 with poly-A RNA.
- Analyzed internal density gradients and diffusion behaviors within droplets.
Main Results:
- LLPS droplets formed by different dipeptides exhibited heterogeneous diffusion.
- Diffusion behaviors strongly correlated with internal density gradients revealed by Raman spectroscopy.
- Charge patterning, not solely net charge, was identified as a critical factor influencing LLPS dynamics.
Conclusions:
- Charge patterning significantly impacts the biophysical properties of biomolecular condensates.
- Raman spectroscopy and FRAP offer a powerful combined approach to study LLPS.
- Understanding these dynamics is crucial for insights into neurodegenerative disease mechanisms.
