# Administrative Supplements for Equipment Purchases for NIGMS-Funded Award: Quantifying Physiologic and Pathologic Viscoelastic Phases of Biomolecular Condensates by Correlative Force and Fluorescence

> **NIH NIH R35** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2022 · $240,544

## Abstract

SUMMARY
In recent years, it has become increasingly clear that the material properties of biomolecular condensates
(BMCs), which are formed via liquid-liquid phase separation, play crucial roles in both cellular physiology and
pathology. Nevertheless, mechanistic understandings of the molecular determinants and modulators of BMC
viscoelastic phases remain incomplete due to the limitations of currently available techniques to probe their
dynamics across single-molecule to mesoscale. The goal of this proposal is to address this critical gap by the
development of a multi-parametric experimental toolbox that simultaneously reports on condensate structure
and dynamics across different length scales, with high sensitivity. Our approach will feature correlative multicolor
single-molecule fluorescence microscopy, single-molecule spectroscopy, dual-trap optical tweezers, and
microfluidics. Utilizing our novel toolbox, we will decipher the mechanisms of liquid-to-liquid and liquid-to-solid
phase transitions of intracellular BMCs, processes that critically contribute to the onset or development of many
neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD).
Commonly used fluorescence microscopy techniques, such as fluorescence recovery after photobleaching
(FRAP), offer only probe-specific protein/RNA diffusivity within the RNP granules. In contrast, our proposed
correlative force-fluorescence microscopy platform will provide a multiscale view of BMC structure and dynamics
by taking advantage of optical tweezer-based rheological and fluid dynamics measurements in conjunction with
quantification of protein/RNA dynamics using single-molecule fluorescence. Recent results from the project
supported by the parent award clearly established that BMCs are network fluids where the network connectivity
and dynamics govern their functional output. These results, in conjunction with our recent discovery that
oncofusion transcription factors reprogram gene expression via ectopic phase separation in the nucleus,
collectively led us to hypothesize that BMC network structure and dynamics from single-molecule-to-mesoscale
precisely orchestrate gene regulation within the nuclear chromatin. Overall, our research program will address
three Key Challenges (KCs): (a) we will develop a novel multi-parametric approach based on correlative single-
molecule fluorescence microscopy, single-molecule spectroscopy, and dual-trap optical tweezer that
simultaneously reports on molecular and mesoscale protein-RNA condensate structure and dynamics in vitro
and in live cells (KC 1), (b) we will apply our toolbox to map the transition pathways of physiologic BMCs to
pathologic states in c9orf72 repeat expansion disorder (KC 2), and (c) we will identify mechanisms of
transcriptional condensate formation, regulation, and function at DNA enhancer sites (KC 3). Our studies will
provide new insights into the determinants of functional BMC materia...

## Key facts

- **NIH application ID:** 10582189
- **Project number:** 3R35GM138186-03S1
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Priya R. Banerjee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $240,544
- **Award type:** 3
- **Project period:** 2020-08-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10582189

## Citation

> US National Institutes of Health, RePORTER application 10582189, Administrative Supplements for Equipment Purchases for NIGMS-Funded Award: Quantifying Physiologic and Pathologic Viscoelastic Phases of Biomolecular Condensates by Correlative Force and Fluorescence (3R35GM138186-03S1). Retrieved via AI Analytics 2026-05-31 from https://api.ai-analytics.org/grant/nih/10582189. Licensed CC0.

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