Measuring sample polydispersity index in unpurified sample matrices.

What is sample polydispersity and polydispersity index?

Sample polydispersity refers to the degree of non-uniformity or variability in the size or properties of particles within a sample. Polydispersity Index (PDI) is a measure used to quantify the polydispersity of a specific sample. It provides a numerical value that describes the particle size distribution relative to the mean particle size. A PDI value of 1 indicates that all particles are the same size (monodisperse), whereas values greater than 1 indicate increasing polydispersity (the particles vary more in size).

What information does the Polydispersity Index (PDI) provide about a sample?

The Polydispersity Index (PDI) serves as a pivotal biophysical parameter, shedding light on the diversity and variability present within a given sample. A polydisperse sample encompasses components of distinct sizes, which can indicate the presence of contaminants, impurities, monomeric units within a polymer, or unbound ligands.

PDI's utility spans across diverse applications, including the evaluation of purification processes, monitoring synthetic polymerisation outcomes, quality control in protein characterisation, and the quantification of binding parameters between molecules forming a complex.

Is there a necessity for a novel method to measure PDI?

In our eyes, yes. The necessity for a novel method to assess PDI arises from the limitations associated with conventional techniques such as mass spectrometry or size-exclusion chromatography. These approaches demand purified samples, necessitating the removal of complex matrices like cell lysates, and typically require substantial sample volumes, thereby diminishing their suitability for routine sample analysis. Moreover, column clogging issues further compound the challenges and frustrations related to these methods.

Thankfully, a pioneering approach grounded in first-principle technology capitalises on molecular diffusion to identify species of varying sizes within complex sample matrices. As a result, distinct size populations manifest as individual peaks within the raw Flow-Induced Dispersion Analysis (FIDA) signal. The incorporation of a "PDI calculator" within the FIDA data analysis software streamlines the calculation of sample dispersity for each FIDA analysis, rendering PDI a readily accessible parameter.

What are the research applications?

The application of FIDA methodology finds its niche in diverse research domains. For instance, it has proven valuable in elucidating the dynamics of multivalent interactions, which play pivotal roles in cellular regulation and immune responses. Multivalent molecules often exhibit agglutination tendencies, making it challenging to distinguish genuine biological interactions from such agglutination, thereby skewing the measurement of the system's biophysical properties. FIDA technology, encompassing Flow-Induced Dispersion Analysis and adjustable mobilization pressure, offers the capability to control complex formation and lifetime. Consequently, it becomes possible to differentiate and study both true multivalent interactions and agglutination. The method empowers researchers to decouple essential parameters, like the dissociation constant (Kd), from oligomerization, facilitating their quantification and enhancing the understanding of multivalent interactions.

Learn more, read the full application note: https://www.fidabio.com/literature/characterizing-multivalent-complex-formation-agglutination-of-multivalent-antibodies