2.7. Nano flow cytometry

Nano flow cytometry uses specialized equipment to apply the fundamentals of standard flow cytometry to sub‐micron particles. The NanoAnalyzer (nanoFCM) has been optimized to allow for side scatter (SSC) measurements of biological particles, such as EVs and viruses, down to 40 nm, in line with the comparative study of Ma et al. (2016). Two additional fluorescent detectors allow for further characterization of particles (Zhu et al., 2014).

A NanoAnalyzer N30 instrument equipped with a single 488 nm laser and single‐photon counting avalanche photodiodes detectors (SPCM APDs) was used for detection of the SSC (bandpass filter: FF01 − 524/24) of individual particles/EVs. HPLC grade water served as the sheath fluid via gravity feed, reducing the sample fluid diameter to ∼1.4 μm. Data were generated through the NanoFCM Professional Suite v1.8 software, with noise being removed through the use of blanks.

Measurements were taken over 1‐min periods at a sampling pressure of 1.0 kPa, modulated and maintained by an air‐based pressure module. Samples were diluted in PBS as required to allow for 2000‐12000 counts to be recorded during this time.

During data acquisition the sample stream is completely illuminated within the central region of the focused laser beam, resulting in approximately 100% detection efficiency, which leads to accurate particle concentration measurement via single particle enumeration (Tian et al., 2020; Zhu et al., 2010).

The concentration of samples was determined by comparison to 250 nm silica nanoparticles of known particle concentration to calibrate the sample flow rate. Sizing calibrations were specific to certain subsets of samples as were laser settings. EV isolate and liposome samples were sized according to standard operating procedures using the proprietary four‐modal silica nanosphere cocktail generated by nFCM to contain nanosphere populations of 68 nm, 91 nm, 113 nm, and 155 nm in diameter. A standard curve is produced by fitting the side scatter intensity vs particle diameter of the four different silica particles with a power function f = c*dⁿ. For the EV and liposome measurements the best fit for the silica cocktail was achieved with an exponent n of approximately 5.3. The produced standard curve is then used to size liposomes and EVs. Please note that in case of Rayleigh approximation that typically applies for particle diameters d smaller than 1/10 of the wavelength of the incident light – in our case of the laser wavelength being 488 nm Rayleigh theory applies for d < 50 nm – the scatter intensity scales with d⁶.

Silica provides a stable and monodisperse standard with a refractive index of approximately 1.43‐1.46 (Hart & Terray, 2003; Van Der Pol et al., 2014; Welsh et al., 2020) which is close to the range of refractive indices reported in literature for EVs (n = 1.37‐1.42) (Tian et al., 2018, 2020; Van Der Pol et al., 2014; Welsh et al., 2020). Using such a calibration standard enables accurate nFCM size measurements, as confirmed when comparing nFCM with cryo‐TEM results (Tian et al., 2018). The refractive index of liposomes is also assumed to be similar to silica, with variations accrued by their lipid and biological composition, remaining important considerations during data interpretation (Matsuzaki et al., 2000). The laser was set to 10 mW and 10% SSC decay.

As polystyrene has a very different refractive index compared with silica, polystyrene samples were sized by comparison to multimodal cocktails of CPN and NIST particles. Side scatter intensities measured for particles in mixed samples (C, I, J) were compared to a trimodal cocktail of CPN 60, CPN100, CPN150. These measurements were taken at a laser power of 25 mW, 0.2% SSC decay, allowing for inclusion of all particles in a single 1‐min measurement.

Data processing was handled within the nFCM Professional Suite v1.8 software, with dot plots, histograms, and statistical data being provided in a single PDF. Gating within the software allows for proportional analysis of subpopulations separated by SSC intensities with PSD and concentrations available for each sub‐population. In cases where additional contaminant particles were observed (past the frequency observed in the blanks) thresholding was applied to remove these from further processing. Conversion of PSD histograms to show particles/ml on the y axis was done by exporting data as CVS files to allow for Excel data conversion. Briefly, histogram data were exported, and number of events was converted from integers to particles per ml using the concentration standard measured.