Methods for characterizing the material properties of biomolecular condensates

Abstract

Biomolecular condensates are membrane-less sub-cellular compartments that perform a plethora of important functions in signaling and storage. The material properties of biomolecular condensates such as viscosity, surface tension, viscoelasticity, and macromolecular diffusion play important roles in regulating their biological functions. Aberrations in these properties have been implicated in various neurodegenerative disorders and certain types of cancer. Unraveling the molecular driving forces that control the fluid structure and dynamics of biomolecular condensates across different length- and time-scales necessitates the application of innovative biophysical methodologies. In this chapter, we discuss major experimental techniques that are widely used to study the material states and dynamics of biomolecular condensates as well as their practical and conceptual limitations. We end this chapter with a discussion on more advanced tools that are currently emerging to address the complex fluid dynamics of these condensates.

Keywords: Condensate viscoelasticity; FRAP; Fluorescence microscopy; Liquid-liquid phase separation; Microrheology; Optical tweezers; Particle tracking.

Fig. 1

Probing droplet coalescence using aspect ratio analysis.

Fig. 2

Optical trap-induced droplet coalescence as a method to measure the relaxation time (a) a schematic diagram showing that the fusion process is induced by two optical traps. (b) a schematic ray diagram showing how the laser deflection (x) of the optical trap varies with droplet shape due to the change in the angle of incidence θ. (c) A representative laser signal from Trap-1 as a function of time during the relaxation process.

Fig. 3

Sample holder preparation for optical trap-induced coalescence experiment.

Fig. 4

Bright-field images of two optically trapped protein condensates as they are driven to undergo coalescence. Trap-1 velocity was set at 40 nm/sec. Scale bar represents 5 μm.

Fig. 5

(a) Raw laser signal recorded from fusing two optically trapped droplets. (b) Trimming the laser signal to selectively extract the fusion-relaxation signal (the exponential phase). The green double-sided arrow shows a reasonable trim. The red double-sided arrow shows an unrecommended trim since most of the points lie in the linear part of the signal and may result in a biased fit. (c) The trimmed signal in (b) is re-plotted. (d) The final laser signal functional form is a combination of an exponential relaxation process and a linear process.

Fig. 6

(a) Fitting (red curve) of the laser signal (black curve) to extract the characteristic time of the relaxation process. (b) Image processing of the two droplets under investigation to extract their average diameter. The fluorescence micrograph is converted to a binary image by applying a threshold and the droplets are fitted with ellipses to extract the diameters (see the provided Matlab script).

Fig. 7

The relaxation time scales linearly with droplet size, with a slope equal to inverse capillary velocity η/γ (Alshareedah et al. unpublished).

Fig. 8

(a) Diagram illustrating the concept of a FRAP experiment. Each green dot represents a single fluorescently-labeled molecule. (b) FRAP recovery curve showing full recovery. The dashed line indicates the recovery half time. (c) FRAP recovery curve showing partial recovery. The red dashed double-sided arrow indicates the recovered intensity [fraction of mobile phase is 50% in (c) and 100 % in (b)]. The blue dashed double-sided arrow indicates the bleaching depth.

Fig. 9

(a) confocal images of a protein-RNA droplet before bleaching, after bleaching, and at a full recovery. The yellow circle indicates the bleaching spot; the cyan circle indicates a reference region in an unbleached droplet. (b-c) Intensity time traces of the reference droplet (red) and the bleached droplet (black) before correction (b) and after correction (c). (d&e) Same as (b&c) but for a sample with strong photofading effects.

Fig. 10

(a) FRAP data fitting for case 1, Figure 9c using equations 5.4 (red) and 5.5 (green). (b) FRAP data fitting for case 2, Figure 9e using equations 5.4 (red) and 5.5 (green).

Fig. 11

(a) Fluorescence image of a protein-RNA droplet immediately after bleaching. (b) Intensity profile along the yellow dashed line in (a) fitted to a Gaussian function (equation 5.7). Residuals are shown. (c) 2D surface plot for the raw intensity values as a function of position (left). The surface fit of the data is generated using equation 5.8.

Fig. 12

(a) A schematic diagram and a bright-field image showing a microsphere is trapped inside a protein droplet. (b) A representative trajectory of a microsphere in a particle tracking experiment. (c) Mean square displacement (MSD) calculated for the trajectory in (b). Red: MSD data; Black: a line with a slope equal to 1.

Fig. 13

MSD data extracted from the trajectory in Figure 12. Red: MSD data. Black: a fitting model using equation 6.3.

Fig. 14

(a) A diagram showing a trapped droplet between two optically trapped microspheres. (b) Driving Trap-1 to undergo oscillation applies stress on the condensate due to repetitive stretching. (c) Simulated data of Trap position, the force from Trap-1 (F₁), the force from Trap-2 (F₂) as a function of time during the oscillatory stress.

Fig. 15

Representative diagram for the frequency-dependent spring constant of a Newtonian fluid droplet. The real part of the spring constant (black) contains information on the surface tension of the droplet and the imaginary part of the spring constant (red) gives information on the viscosity.