Combining Interferometric Nanoparticle Tracking Analysis (iNTA) with Fluorescence detection to Distinguish Nanoparticle Sub-Populations with Single-Molecule Precision
Background
The characterization of biological particles, such as extracellular vesicles (EVs), is crucial for understanding their roles in biological processes and their diagnostic potential. However, their heterogeneous composition poses challenges on the quantification of their biophysical and biochemical properties.
Methods
Recently, we presented interferometric nanoparticle tracking analysis (iNTA) as a label-free method for characterizing nanoparticles such EVs. Here, we show that by combining iNTA with sensitive fluorescence detection, one can not only characterize the EVs but also their biomolecular content. Additionally, we use a flow system for the sample loading process in order to mitigate bleaching of fluorescence signals. This approach enables us to simultaneously determine the size, refractive index, and intensity of the fluorescent labels of single particles.
Results
As a proof of principle, we have shown that we can differentiate nanoparticles in the presence of fluorescence by using a mixture of 40nm non-fluorescent polystyrene beads and 100nm fluorospheres. We examined the fluorescence response of our setup by measuring liposomes labeled with two different lipid dyes, MemBright (for 488nm excitation) and R18 (for 561nm excitation). We quantified the fluorescence intensity of each liposome, finding it to be proportional to the liposome size and the amount of the dye used. Finally, we labeled EVs derived from HEK293 cells with anti-CD9 and anti-CD81 fluorescent antibodies, enabling us to quantify the percentage of EVs expressing these biomarkers.
Conclusion
Our findings suggest that our multimodal analysis approach can significantly enhance one’s ability to distinguish multiple populations within heterogeneous samples, thus, contributing to the advancement of technologies for biomarker discovery.
Keywords
iNTA, fluorescence, multimodal analysis, biomarker discovery, quantification
Funding/Acknowledgments
Max Planck Society
Authors
Hannarae Lee1,2,3, Shuhan Jiang1,3, Anna Kashkanova1,3, Morgan Miller1,3, Vahid Sandoghdar1,2,3 (Corresponding Author:
vahid.sandoghdar[at]mpl.mpg[dot]de)