Single particle profiler for measuring content and properties of nano-sized bioparticles

Single particle profiler was developed for high throughput, single particle analysis of nanometer-sized bioparticles using the conventional confocal microscopy setup.
Published in Protocols & Methods
Single particle profiler for measuring content and properties of nano-sized bioparticles

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Techniques based on flow cytometry provide high throughput capabilities of single cell analysis of high statistical significance. Unfortunately, similar methodologies for nanometer sized bioparticles such as extracellular vesicles, viruses and lipoproteins require further developments. For now, most of them require specific equipment based on microfluidics and specific optical setups1,2, whereas conventional flow cytometry equipment does not have capabilities to process nanometer sized particles in single-particle manner. Here, we developed the single particle profiler (SPP) which allows for analysis of content and biophysical properties of nanometer-sized bioparticles in single particle manner using a standard instrument of many life science laboratories – the confocal microscope.

How does SPP work?

We prepare the solution of fluorescently labeled particles and observe their free diffusion across the observation volume (figure 1a), which is the confocal laser volume of the microscope by recording the fluorescence emission traces over time (figure 1b). Further analysis of those traces yields the fluorescence intensity per particle. It is possible to perform these measurements in two- and multicolor manner, hence simultaneous analysis of various components of single particles is possible.


Figure1: a) microscopy setup for SPP; b) examples of fluorescence intensity traces in two color channels with marked detected peaks


What can we analyze with SPP?

Any nanometer sized particle smaller than the observation volume can be an eligible candidate for examination with SPP. Lipid and protein content of viruses and extracellular vesicles can be explored, as well as the antibody binding to viral particles can be thoroughly investigated to, for example, study the immune response. In addition, the field of drug delivery can hugely benefit from this method, as it can report on the efficiency of encapsulation of drugs into lipid nanoparticles3. We demonstrated the applicability of this method on comparing the encapsulation of mRNA (labeled with far red label) into lipid nanoparticles (LNPs), labeled with green lipophilic marker (figure 2a). For such system, empty LNPs as well as free mRNA are present in the sample. All three scenarios can be clearly observed in the recorded two-color intensity trace (figure 2b). From this trace, for every particle we can calculate the encapsulation efficiency (figure 2c). Moreover, SPP can identify the fluorescence signal of mRNA per particle which enables the approximation of mRNA copies per particle using proper calibration.  

Figure2: a) cartoon of mRNA encapsulated into LNPs; b) fluorescence intensity traces in two color channels with peaks marked for different scenarios; c) mRNA encapsulation efficiency depending on lipids to RNA ratio; d) ratiometric histograms that represent amount of mRNA per particle.


Which biophysical parameters can be extracted with SPP?

First clear advantage of SPP is that it is a method based on free diffusion of particles in the solution which implies the analysis of intensity trace with autocorrelation, as in fluorescence correlation spectroscopy (FCS)4. The resulting diffusion coefficient of the particles in water can point towards hydrodynamic properties of the particle and to its shape and size. Second, using environmental sensitive probes in combination with the ratiometric analysis5, we can report on lipid packing and/or membrane fluidity in the particle. We tested this by studying artificial liposomes with distinct lipid compositions that have different membrane fluidities (figure 3a). Moreover, we studied the heterogenous mixture of two liposome species in one sample and observed very clear two-population histogram (figure 2b). This shows the clear advantage of SPP over the traditional bulk measurements that are widely used for similar experiments. Finally, we isolated lipoproteins from the blood plasma of apparently healthy donors and studied their diffusion in water and fluidity of their lipid shells. We observed that we can clearly separate lipoproteins of different densities (high, low and very low) by their diffusion coefficients and fluidities (figure 3c).


Figure3: a) GP histograms for liposomes of different compositions; Width of GP distributions (inlet); b) histogram of mixture of POPC and DPPC/Chol liposomes with clear two populations; c) Diffusion vs. GP diagram for lipoproteins from healthy individuals,


Besides lipoproteins, we studied the membrane fluidity of extracellular vesicles extracted from different cells, the impact of protein content on the membrane fluidity of VLP envelope, as well as impact of lipid content on membrane fluidity of lipid nanoparticles.

In future we aim to apply SPP for analysis of biophysical parameters of lipoproteins and exosomes in health and disease (e.g. lipid packing of lipoproteins in metabolic diseases), deciphering the lipid and protein content of VLPs and EVs as well as studying the vaccine immune response for viruses.


  1. Morales-Kastresana, A. et al. High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer. Journal of Extracellular Vesicles 8, 1597603 (2019).
  2. Andronico, L. A. et al. Sizing Extracellular Vesicles Using Membrane Dyes and a Single Molecule-Sensitive Flow Analyzer. Anal. Chem. 93, 5897–5905 (2021).
  3. Hou, X., Zaks, T., Langer, R. & Dong, Y. Lipid nanoparticles for mRNA delivery. Nat Rev Mater 6, 1078–1094 (2021).
  4. Eigen, M. & Rigler, R. Sorting single molecules: application to diagnostics and evolutionary biotechnology. PNAS 91, 5740–5747 (1994).
  5. Kucherak, O. A. et al. Switchable Nile Red-Based Probe for Cholesterol and Lipid Order at the Outer Leaflet of Biomembranes. J. Am. Chem. Soc. 132, 4907–4916 (2010).

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