NanoSight Pro size distribution analysis – FTLA or Raw?

Sizing of nanoparticles using Nanoparticle Tracking Analysis (NTA)

Nanoparticle Tracking Analysis is known for high-resolution particle-by-particle distribution, but what exactly do we mean by “high-resolution”? This technical note explores two methods for plotting distribution data with NanoSight Pro, aimed at enhancing confidence in the analysis of polydisperse particles.

Particles are seen moving under Brownian motion within the NS Xplorer software’s field of view. The software records a 25-60-second video, simultaneously identifying and tracking visible particle centers on a frame-by-frame basis. The software calculates the average distance each particle moves along the x and y planes, thus calculating the mean square displacement (MSD). MSD is subsequently translated to the hydrodynamic diameter (dh) through the Stokes-Einstein equation (Equation 1).

Equation 1: Stokes-Einstein equation where MSD is mean-square displacement of particles, KB is Boltzmann Constant, T is temperature, η is viscosity, and dh is the hydrodynamic diameter.

The NS Xplorer (and previous NTA software versions) algorithm must track a particle through enough steps of Brownian motion to accurately determine the average step length and thus hydrodynamic size. Small particles, however, are limited by the scattering volume and the limited time they remain within the field of view (i.e. <10 frames = 0.3 seconds at 30 frames per second (fps)). These limitations occasionally manifest as artificial distribution broadening while the mean size remains accurate. To address this, NS Xplorer and the NTA software employ a mathematical model that compensates for limited trajectories in Brownian Motion (Saveyn et al, 2010). This model is known as “finite track length adjustment” (FTLA). FTLA compensates for the broadening effects and presents an adjusted distribution width (Figure 1).

Figure 1: FTLA model is appropriate for samples that contain multiple discrete and resolvable populations. FTLA further helps extract the monodispersed size distribution of 100nm latex beads.

FTLA typically applies to monodisperse samples or polydisperse samples with discrete populations of bimodal or trimodal mixtures. RAW distributions provide greater insight for more polydisperse samples and assure good measurement reproducibility in more complex systems.

Figure 2 illustrates the increased polydispersity in complex samples between the FTLA (Blue) and RAW (Red) distributions. The raw data reflects the sample’s ‘true’ heterogeneity with a wide size distribution without compromising the resolution of individual shoulder peaks of the main distribution. 

Figure 2: RAW model (RED) is appropriate for samples that are polydisperse resulting in a continuous size distribution.

The figures below further illustrate how FTLA and RAW distributions affect data reproducibility.

Figure 3: Exosomes size distribution presented as RAW and FTLA

Figure 4: Exosomes FTLA size distribution showing decreased reproducibility.

RAW analyses might better suit more polydisperse populations where FTLA might accentuate discreet populations and thus reduce reproducibility between measurements (Figure 4).

The FTLA algorithm is based on predicting individual path lengths for each particle for an infinite period of time, affording a narrow size distribution peak. The FTLA algorithm groups particles with similar sizes into different distribution bins. However, this proves more challenging when the samples are highly polydisperse, as the varying particle sizes complicate the differentiation between size bins. The final distribution plot looks less reproducible across different measurements with FTLA.  Plotting RAW distributions mitigates this variation, thus presenting better reproducibility across numerous sample measurements (Figure 5).

Figure 5: Exosomes RAW size distribution showing increased reproducibility.

Both FTLA and RAW modes are useful for assessing discrete populations.  The RAW distribution might present an individual peak that may or may not seem like a shoulder to the side of the main population (Figure 6). Switching from RAW to FTLA further confirms the discreet presence of the second peak from the main population, indicating that a significant portion of the sample has a larger diameter (Figure 7).

Figure 6: RAW size distribution of nanobubbles.

Figure 7: FTLA size distribution of nanobubbles.

Summary

The table below summarizes when you should choose FTLA or RAW analysis for your size distributions with nanoparticle tracking analysis.