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Determination of particle number concentration for biological particles using AF4-MALS: Dependencies on light scattering model and refractive index
Summary
Researchers investigated how light scattering models and particle refractive index assumptions affect particle number concentration determinations for biological particles using asymmetrical flow field-flow fractionation with multiangle light scattering (AF4-MALS). They found that the choice of scattering model and refractive index values introduces quantifiable bias in concentration estimates, with implications for standardizing bioparticle characterization across laboratories.
Determining accurate counts and size distributions for biological particles (bioparticles) is crucial in wide-ranging fields, but current ensemble methods to this end are susceptible to bias from polydispersity in size. This bias can be mitigated by incorporating a separation step prior to characterization. For this reason, asymmetrical flow field-flow fractionation (AF4) with on-line multiangle light scattering (MALS) has become an important platform for determining particle size. AF4-MALS has been used to report particle concentration, particularly for complex biological particles, yet the impact of light scattering models and particle refractive indices (RI) have not been quantitatively assessed. Here, we develop an analysis workflow using AF4-MALS to simultaneously separate and determine particles sizes and concentrations. The impacts of the MALS particle counting model used to process data and the chosen RI value(s) on particle counts are systematically assessed for polystyrene latex (PSL) particles and bacterial outer membrane vesicles (OMVs) in the 20-500 nm size range. Across spherical models, PSL and OMV particle counts varied up to 13% or 200%, respectively. For the coated-sphere model used in the analysis of OMV samples, the sphere RI value greatly impacts particle counts. As the sphere RI value approaches the RI of the suspending medium, the model becomes increasingly sensitive to the light scattering signal-to-noise ultimately causing erroneous particle counts. Overall, this work establishes the importance of selecting appropriate MALS models and RI values for bioparticles to obtain accurate counts and provides an AF4-MALS method to separate, enumerate, and size polydisperse bioparticles.
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