Characterising the bio-nano interface using QCM technology. By Teodor Aastrup, Diluka Peiris & Daniel Wallinder
Manufactured nanoparticles (MNPs) are increasingly being considered for use in biomedical applications ranging from drug delivery to cellular imaging. Thus, the understanding of MNPs’ interactions with biological systems has become vital for both their safety profile and efficient applications.
The growing interest on elucidating the impact of physicochemical properties of NPs (eg, size, surface charge, hydrophobicity, or shape) on their subsequent cellular interactions necessitates the exploration of new technical tools.
QCM technology
Here we illustrate that these bio-physiochemical interactions can be investigated by Attana´s cell-based quartz crystal microbalance (QCM) technology, a label-free method widely used to study binding between two macromolecules. The QCM technology is a sensitive balance capable of measuring changes in mass at a molecular level.
An applied AC potential causes the quartz crystal to vibrate at its resonance frequency.
As molecules flow over the crystal and bind to their receptor/ligand the vibration frequency changes. This change in frequency is used to characterise real-time, label-free molecular interactions.
Typically, one of the two interacting partners is immobilied on a sensor chip surface, and the other is flowed through a microfluidic system in contact with the chip surface.
Binding is revealed in real time as a change of mass at the surface, and the interaction can be characterised in terms of on and off rates (kinetics) and binding strength (affinity).
In the Attana Cell 200, innovative experiments are facilitated, for instance, the study of bimolecular interactions directly on cell surfaces and the utilisation of complex biological samples including serum(1).Thanks to these features, Attana QCM technology allows characterisation of NP interaction in a physiologically relevant environment.
Characterisation of NPs’ interaction with adherent cells
Nanoparticles that are taken up in the body will come into contact with extracellular proteins such as blood serum proteins.
These proteins can be rapidly absorbed on to the surface of nanoparticles, forming a protein coating refers as ‘protein corona’.
The protein corona signifies the biological identity of the nanomaterial and alters nanoparticle-cell surface interactions compared to the native nanoparticle.
A new approach
A platform based on the Attana QCM based cell biosensor technology, which utilises adherent cells directly grown on the sensor surface has been developed for the characterisation of interactions at the bio-nano interface (2), (3).
Fig. 1. shows the formation of protein coating and binding of nanoparticles to cell surface. NPs were dispersed in 10% fetal calf serum.
Serum protein coated NPs were injected over lung epithelial cells grown on sensor surface. Within the figure, (a) and (b) show the binding curves of the same type of NPs either conjugated with COOH groups or NH2 groups resulting net positive or negative surface charge.
As evidenced from the binding curves – shown in Fig. 1. (a) and (b) – the surface charge of the NPs clearly influence the binding kinetics. The results show the applicability of Attana’s innovative cell-based biosensor system for the studies of interactions at the bio-nano interface.
Furthermore, the results indicate that this platform could also be used to investigate the impact of the physiochemical properties of NPs on their bio-molecular interactions.
Determination of kinetics and affinity of NPs interaction
Due to the ambiguity of molecular weight of the corona coated particles, the association rate and affinity can only be calculated after weight determination of the particles.
Alternatively, the kinetic parameters and affinity of the interactions between NPs and their receptors can be calculated if the system is reversed and set in a biochemical assay.
Experiments have been conducted using the standard biochemical format, where one binding partners is immobilised onto the sensor surface.
Fig. 2. shows the interaction between antigen A and shMFE conjugated NPs and free shMFE.
shMFE conjugated NP was immobilised onto the sensor surface using amine coupling chemistry and binding was studied by injecting the antigen A at five concentrations in duplicates.
After each sample injection the sensor chip was regenerated. Binding of free shMFE onto antigen A was also investigated following the above procedure. Raw data was processed using evaluation software, a part of the Attaché software suite. Data used for deriving kinetic rate constants was referenced by subtracting data from buffer injections and referenced with channel B. Kinetic rate constants were derived using a 1:1 binding model.
The derived kinetic rate constants and the calculated affinity of the two interactions are very similar, indicating the recognition of shMEF by antigen A. The process of conjugation does not seem to interfere with the interaction of the two molecules, see Fig. 2. (a), (b). As evidenced from Fig. 2., the kinetic rate constants and affinity values are associated with small errors.
What can we learn from these results?
In conclusion, the cell-based and biochemical assay platforms Attana has developed have demonstrated the potential to become the technique of choice when investigating the interactions at bio-nano interfaces. This approach can be used in more detailed experiments concerning bio-nano interface, for instance identification of binding partners of the NPs.
The label-free Attana Cell 200 biosensor is a key component of Attana’s vision to provide the ability to measure molecular kinetic interactions in biologically relevant systems. The ‘one-stop-shop’ instrument makes it possible to combine the strengths of both high quality biochemical and cell-based assays in one system.
For more information at www.scientistlive.com/eurolab
Teodor Aastrup, Diluka Peiris & Daniel Wallinder are with Attana in Stockholm, Sweden.
References: 1 Aastrup, T (2013). Innovations in Pharmaceutical Technology, pp. 48-51; 2 Louise L., Käck, C., Aastrup, T., Nicholls, I.A (2015). Sensors. 15, 5884-5894; 3 Peiris, D., Markiv, A., Curley, P., Dwek, M.V. (2012) Biosensors and Bioelectronics. 35:160-166
