To evaluate the nanoparticle tracking analysis (NTA) technique, compare it with dynamic light scattering (DLS) and test its performance in characterizing drug delivery nanoparticles and protein aggregates.
Standard polystyrene beads of sizes ranging from 60 to 1,000 nm and physical mixtures thereof were analyzed with NTA and DLS. The influence of different ratios of particle populations was tested. Drug delivery nanoparticles and protein aggregates were analyzed by NTA and DLS. Live monitoring of heat-induced protein aggregation was performed with NTA.
NTA was shown to accurately analyze the size distribution of monodisperse and polydisperse samples. Sample visualization and individual particle tracking are features that enable a thorough size distribution analysis. The presence of small amounts of large (1,000 nm) particles generally does not compromise the accuracy of NTA measurements, and a broad range of population ratios can easily be detected and accurately sized. NTA proved to be suitable to characterize drug delivery nanoparticles and protein aggregates, complementing DLS. Live monitoring of heat-induced protein aggregation provides information about aggregation kinetics and size of submicron aggregates.
NTA is a powerful characterization technique that complements DLS and is particularly valuable for analyzing polydisperse nanosized particles and protein aggregates.
Most macromolecular drugs, such as proteins, peptides, DNA and RNA, cannot be administrated via the traditional oral route of administration, due to their susceptibility to enzymatic degradation or low absorption efficiency (
DDSs not only protect the therapeutic drug or antigen from degradation and/or increase their absorption, but may also allow controlled release or the precise delivery to a specific target (
Another pharmaceutically important field that requires adequate tools for the analysis of particles in the nanometer size range is the characterization of protein aggregates. Therapeutic proteins are prone to several chemical and physical degradation pathways, which often lead to aggregation (
The most commonly used techniques for the analysis of nanoparticles and protein aggregates include dynamic light scattering (DLS), scanning electron microscopy (SEM), size exclusion chromatography (SEC), gel electrophoresis, asymmetrical flow field-flow fractionation (AF4) and analytical ultracentrifugation (AUC) (
Despite being a powerful and accessible tool, DLS is also known to have several drawbacks, which are mainly inherent to the principles of the technique. Particle size is determined from fluctuations in scattered light intensity due to the Brownian movement of the particles (
Nanoparticle tracking analysis (NTA), which was first commercialized in 2006, is an innovative system for sizing particles from about 30 to 1,000 nm, with the lower detection limit being dependent on the refractive index of the nanoparticles. This technique combines laser light scattering microscopy with a charge-coupled device (CCD) camera, which enables the visualization and recording of nanoparticles in solution. The NTA software is then able to identify and track individual nanoparticles moving under Brownian motion and relates the movement to a particle size according to the following formula derived from the Stokes-Einstein Eq.
where
Our aim was to explore the potential of nanoparticle tracking analysis (NTA) for the analysis of nanosized particles and protein aggregates. A direct comparison with DLS was made in order to reveal the advantages and pitfalls of a technique that is now making its first steps in the field of characterization of nanoparticles and submicron protein aggregates.
Poly (lactic-co-glycolic acid) 50:50 (PLGA) and 4-(2-hydroxyethyl)-1-piperazine-ethanesulfonic acid (HEPES) were obtained from Sigma-Aldrich (Steinheim, Germany), chitosan (deacetylation degree 92%, MW 120 kDa) from Primex (Siglufjordur, Iceland) and egg L-α-phosphatidyl choline (EPC) from Lipoid GmbH (Ludwigshafen, Germany). 1,2-dioleoyl-sn-glycero-3-phospho ethanolamine (DOPE) and 1,2-dioleoyl-3-trimethyl ammonium-propane (DOTAP) were supplied by INstruchemie (Delfzijl, The Netherlands). Chloroform was purchased from Biosolve (Valkenswaard, The Netherlands). All other chemicals used were from Sigma-Aldrich (Steinheim, Germany), unless mentioned otherwise.
Polystyrene nanometer standard beads with sizes of 60, 100, 200, 400 and 1,000 nm were purchased from Thermo Scientific (Fremont, USA). They were diluted from the supplied package in deionized water until the concentration was acceptable for NTA measurements, i.e. between 107 and 109 total particles/ml. Thus, from the supplier’s recipient, a 1:30,000 volume based dilution was made for the 60-nm beads, 1:100,000 dilution for the 100-nm beads, 1:25,000 dilution for the 200-nm beads, 1:2,500 dilution for the 400-nm beads, and 1:100 dilution for the 1,000-nm beads. All polystyrene bead measurements were performed with these samples, either alone or mixed at different volume ratios or number ratios based on NTA particle counts, as stated in the results section.
The 100-nm and 400-nm beads mixture used for the spiking experiments contained about 1.7*108 beads/ml. For these experiments, 2 or 40 μl of a suspension of 1,000-nm beads (ca. 1.6*108 particles/ml) were added to 500 μl of the 100-nm and 400-nm beads mixture, which resulted in a 1,000-nm beads concentration of about 6.4*105 beads/ml and 1.2*107 beads/ml, respectively. The resulting number ratios of 1,000-nm beads to the beads in the initial mixture was 1:267 for the 2 μl spike (small spike) and 1:13 for the 40 μl spike (big spike).
N-trimethyl chitosan (TMC) with a degree of quaternization of 15% was prepared from chitosan and used to make TMC nanoparticles, as described in the literature (
PLGA nanoparticles were prepared by an “oil-in-water” solvent evaporation method, using polysorbate 20 as emulsifying agent. Briefly, 1 ml of dichloromethane containing 50 mg of PLGA and 2 ml 1% (w/v) polysorbate 20 were emulsified using an ultrasonic processor for 15 s at 70 W (Branson Instruments, Connecticut, USA). The emulsion was transferred to 50 ml of 0.02% (w/v) polysorbate 20 in water and stirred at 50°C for 1 hr. The resulting PLGA nanoparticles were collected by centrifugation (8,000 g for 10 min) and washed twice in distilled water to remove excess polysorbate 20. The sample was diluted 2000-fold with deionized water before the measurements.
Cationic liposomes were prepared by the film-hydration-rehydration method and sized by sonication. In detail, a lipid film was formed by solvent evaporation of a chloroform solution of EPC, DOPE and DOTAP in a rotary evaporator at 37°C. To prepare 1 ml of liposome dispersion, a total amount of 28 μmol lipid was used at a EPC/DOPE/DOTAP molar ratio of 4/2/1. The film was hydrated in 1 ml of 20 mM HEPES, 5% glucose, pH 7.4, and the dispersion was equilibrated for 1 hr at room temperature. The dispersion was then sonicated twice for 30 s, with 30 s interval, using a Branson Sonifier 250 (Branson Ultrasonics, Danbury, UK), with 3 mm microtip at 20 mW energy output. The sample was diluted 10,000-fold with deionized water before the measurements.
All the buffers used in this section were filtered using a 0.22-μm PES low binding syringe-driven filter unit (Millex™ GP, Millipore, Ireland), and the absence/very low content of submicron particles was confirmed by their visualization in the NanoSight sample chamber.
A recombinant human monoclonal antibody of the IgG1 subclass (IgG) was used for preparing IgG aggregates. The IgG was formulated at a concentration of 1.0 mg/ml in 10 mM sodium citrate (Merck, Darmstadt, Germany), 5% (w/v) sucrose (Sigma-Aldrich, Buchs, Switzerland), pH 6.0. The IgG formulation was filtered using a 0.22-μm PES low binding syringe-driven filter unit. One ml of the filtered IgG formulation was placed in 1.5-ml reaction tubes (Eppendorf, Hamburg, Germany) and incubated for 15 min at 74°C in a heating block (Eppendorf, Hamburg, Germany). The sample was diluted 50-fold with the formulation buffer before each measurement.
Recombinant human insulin (insulin) was formulated in 10 mM sodium phosphate, pH 7.4, and the formulation was filtered using the same filter unit as for the IgG formulation. Insulin aggregation was induced via a metal-catalyzed oxidation reaction by the addition of copper chloride (0.04 mM), followed by ascorbic acid (4 mM). The formulation was incubated at room temperature for three hours, and the reaction was stopped by the addition of 1 mM ethylenediaminetetraacetic acid (EDTA).
All the buffers used in this section were filtered using a 0.22-μm PES low binding syringe-driven filter unit, and the absence/very low content of submicron particles was confirmed by their visualization in the NanoSight sample chamber.
DLS measurements were performed with a Malvern Zetasizer Nano ZS (Malvern, Herrenberg, Germany) equipped with a 633-nm He-Ne laser and operating at an angle of 173°. The software used to collect and analyze the data was the Dispersion Technology Software version 6.01 from Malvern. Five-hundred μl of each sample was measured in single-use polystyrene half-micro cuvettes (Fisher Emergo, Landsmeer, The Netherlands) with a pathlength of 10 mm.
The measurements were made at a position of 4.65 mm from the cuvette wall with an automatic attenuator and at a controlled temperature of 25°C. For each sample, 15 runs of 10 s were performed, with three repetitions for all the polystyrene beads and six repetitions for the polymer nanoparticles, liposomes and protein aggregates. The intensity size distribution, the Z-average diameter (Z-ave) and the polydispersity index (PdI) were obtained from the autocorrelation function using the “general purpose mode” for the monodisperse polystyrene beads, liposomes and polymer samples, the “multiple narrow mode” for the mixtures of polystyrene beads and the “protein analysis mode” for the protein aggregates. The default filter factor of 50% and the default lower threshold of 0.05 and upper threshold of 0.01 were used. The error bars displayed on the DLS graphs were obtained by the standard deviation (SD) of three or six measurements of the same sample.
NTA measurements were performed with a NanoSight LM20 (NanoSight, Amesbury, United Kingdom), equipped with a sample chamber with a 640-nm laser and a Viton fluoroelastomer O-ring. The samples were injected in the sample chamber with sterile syringes (BD Discardit II, New Jersey, USA) until the liquid reached the tip of the nozzle. All measurements were performed at room temperature except the live monitoring protein heat stress measurements (see section below).
The software used for capturing and analyzing the data was the NTA 2.0 Build 127. The samples were measured for 40 s with manual shutter and gain adjustments. The “single shutter and gain mode” was used to capture the monodisperse polystyrene beads, the 60/100 nm beads mixture, the liposomes, the TMC particles and the protein aggregates. The “extended dynamic range mode,” which splits the capture video into two videos with independent shutter and gain settings, was used for all the other mixtures of monodisperse polystyrene beads, the PLGA particles and the insulin aggregates. Three measurements of the same sample were performed for all the polystyrene beads and six measurements for the polymer nanoparticles and protein aggregates. The error bars displayed on the NTA graphs were obtained by the standard deviation of the different measurements of each sample. The mean size and SD values obtained by the NTA software correspond to the arithmetic values calculated with the sizes of all the particles analyzed by the software.
For the live monitoring of protein aggregation, the above-mentioned IgG was formulated at a concentration of 1.0 mg/ml in 100 mM sodium citrate (Merck, Darmstadt, Germany), pH 7.6. Unstressed IgG was inserted in the NanoSight sample chamber at room temperature, and the heating block was then set to 50°C. Once the chamber had reached the set temperature, which took about 10 min, a video was recorded for 40 s (t0), followed by three time points with the same video length, at 15, 25 and 35 min. The videos of the first three time points were captured with the “single shutter and gain mode” and of the last time point with the “extended dynamic range mode,” because of the high sample polydispersity observed for this time point.
In order to verify the accuracy of NTA to size monodisperse samples, standard polystyrene beads of 60 nm, 100 nm, 200 nm, 400 nm and 1,000 nm were analyzed with NTA, and the results were compared to DLS (Fig. Size distribution from NTA and DLS measurements of monodisperse polystyrene beads. Error bars represent standard deviations obtained from three measurements of the same sample.
Contrary to DLS, NTA enables sample visualization and provides approximate particle concentrations, which are very useful features. Both techniques showed good sizing accuracy and relatively narrow distributions for all monodisperse samples. Nevertheless, it is possible to observe a tailing of all DLS size distributions towards larger sizes, mostly due to the immense contribution of a few large particles to the overall scattering (
The mean size values obtained by NTA are slightly smaller and closer to the expected values than the Z-ave given by DLS, but all values are close to the bead size specified by the manufacturer (Table Mean Size and Size Distribution of Monodisperse Polystyrene Beads from NTA and DLS MeasurementsBead size (nm) DLS NTA Z-ave (nm) PdI Mean (nm) SD (nm) Particle conc. (E8/ml) 60 68 ± 1 0.04 ± 0.01 66 ± 2 20 ± 1 7.70 100 112 ± 4 0.13 ± 0.07 105 ± 6 30 ± 10 1.87 200 218 ± 1 0.04 ± 0.01 200 ± 5 30 ± 5 2.32 400 443 ± 5 0.13 ± 0.03 394 ± 7 62 ± 6 1.08 1,000 1056 ± 164 0.36 ± 0.08 989 ± 51 117 ± 14 1.64
While DLS measurements are fast and rather straightforward, NTA requires several optimization steps by a skilled operator, e.g. with respect to indentifying suitable settings for the video capture and analysis. Whereas DLS can automatically adjust the attenuator to adapt to a wide range of sample concentrations, the search for the right sample concentration for a successful NTA measurement can be time-consuming, as it may require various dilution steps. However, NTA proved to be slightly more accurate than DLS for sizing monodisperse samples.
One of the well-known pitfalls of DLS is its low peak resolution, i.e. it can only resolve particle populations that differ in size at least by a factor of 3 ( Size distribution from NTA and DLS measurements of mixtures of monodisperse polystyrene beads (
From these results, the difficulty of DLS in resolving peaks of polydisperse samples becomes apparent, as it was not possible to separate the two bead sizes of any of the mixtures. On the other hand, NTA was able to resolve and distinguish the two size populations in all mixtures and yielded accurate size estimations of the beads in the mixtures (Table Mean Size and Size Distribution of Mixtures of Monodisperse Polystyrene Beads from NTA and DLS MeasurementsBead size (nm) DLS NTA Z-ave (nm) PdI Peak (nm) Mean (nm) SD (nm) Peak 1 (nm) Peak 2 (nm) Peak 3 (nm) 60 + 100 84 ± 1 0.08 ± 0.01 83 ± 7 90 ± 3 33 ± 4 58 ± 4 91 ± 9 – 100 + 200 195 ± 2 0.40 ± 0.20 194 ± 5 162 ± 13 60 ± 6 98 ± 2 196 ± 2 – 200 + 400 347 ± 42 0.19 ± 0.03 359 ± 32 298 ± 20 122 ± 9 195 ± 5 410 ± 10 – 400+1,000 952 ± 31 0.26 ± 0.05 912 ± 75 712 ± 36 296 ± 20 427 ± 46 1067 ± 94 – 100 + 400 430 ± 34 0.29 ± 0.08 363 ± 29 265 ± 16 157 ± 11 98 ± 5 419 ± 12 – 100 + 400+ S. Spike 467 ± 22 0.35 ± 0.05 378 ± 32 338 ± 36 163 ± 23 99 ± 10 384 ± 12 850 ± 83 100 + 400 + B. Spike 698 ± 26 0.14 ± 0.07 750 ± 65 527 ± 101 353 ± 11 106 ± 4 420 ± 22 997 ± 31
The two different bead sizes with different scattering intensities can be observed in the NTA video frames and 2D size distribution graphs and can be clearly distinguished in the 3D graphs (Fig.
Measuring the mixture of monodisperse beads with DLS is as easy and fast as measuring the standard polystyrene beads alone, but the results do not reflect the samples’ real content. On the other hand, NTA analysis of two different particle sizes implies in most cases the use of the “extended dynamic range mode,” which adds a complex variable to the analysis. This mode allows the recording of a set of two videos at the same time with different shutter and gain settings, enabling the simultaneous analysis of large and small particles in one measurement. A big advantage of NTA is the unbiased high peak resolution for polydisperse samples, which is not possible by DLS.
In the field of nanoparticle characterization, it is important to have tools that are able to detect and characterize small amounts of a certain particle size population, different from the main population. Thus, to elucidate the ratio detection limits of NTA, 100 and 400 nm polystyrene beads were mixed at 100-nm/400-nm bead number ratios of 3:1, 6:1, 15:1, 150:1 and 300:1, and analyzed with NTA and DLS (Fig. Influence of different number ratios of 100-nm/400-nm monodisperse beads in NTA and DLS measurements (
The selection of the distinct sizes of beads for these measurements took into consideration the DLS theoretical peak resolution of 3:1 in size. Nonetheless, DLS was not able to distinguish the two bead sizes for the lower 100-nm/400-nm bead number ratios of 3:1 and 6:1, for which mainly the larger beads were detected. When the number ratio reached 300:1, the larger particles were no longer shown by DLS.
On the other hand, analysis with NTA enabled accurate sizing and a clear distinction of the two size populations for all the ratios analyzed. The presence of the two distinct size populations is very clear in the video frames of Fig.
One of the main concerns of DLS is the influence that a small amount of large particles, such as dust, may have on the outcome. The NTA technique is based on the tracking of single particles, whereas DLS measures a bulk of particles with a strong bias to the largest particles present in the sample. Therefore, the performance of NTA is expected to be less sensitive than DLS to the presence of minute amounts of large particles. To compare the influence of large particles on NTA and DLS results, a mixture of 100-nm and 400-nm polystyrene beads was spiked with two different amounts of 1,000-nm beads. The resulting number ratios of 1,000-nm beads to the beads in the initial mixture was 1:267 for the small spike and 1:13 for the big spike (Fig. Influence of large particles (1,000-nm beads) in a mixture of 100-nm and 400-nm monodisperse beads on NTA and DLS measurements. The size distribution (
The small spike was sufficient to cause an increase of about 40 nm in the Z-ave and of about 0.05 in the PdI determined by DLS (Table Effect of the Most Important NTA Software Settings on Size AnalysisParameter Description Impact on the measurement Incorrect settings Optimal settings Shutter/Gain Shutter determines length of time camera shutter is open, gain regulates sensitivity of the camera - Underexposed videos omit smaller particles All particles appear as clear moving dots, traceable by the software - Overexposed videos show particles as untraceable wobbling light flares Capture Duration Determines the length of the captured video - Too short videos result in inaccurate and statistically poor size distributions Statistically robust and reproducible size distributions Blur Smoothes captured video, eliminating visual noise from around and within the particle - Excessive blur makes small particles disappear Removes false light scattering centers, helps software track real particles - Insufficient blur leaves false light scattering centers Detection Threshold Determines minimum gray scale value of a dot necessary for it to qualify as a traceable particle - Too high thresholds result in loss of small particles All particles in the video are tracked, visual noise is ignored - Too low thresholds result in loss of large particles or mistracking of visual noise Minimum Expected Particle Size (MEPS) Determines area around the particle where the software searches for it in the following frame (large particles—slow movements—small areas; small particles—fast movements—large areas) - Too high MEPS result in loss of small particles because they move outside of the search area Most particles in the video are tracked long enough to be included in final result - Too low MEPS result in loss of particles in general due to search area overlap
The big spike increased the Z-ave of DLS from 430 to 698 nm, but curiously decreased the PdI from 0.29 to 0.14. The lower PdI of this spiked sample is most likely a consequence of the masking effect of the 1,000 nm beads over the smaller beads in DLS measurements. These DLS values could have suggested that it was a fairly monodisperse 700 nm sample, when in fact it contained three distinct size populations of 100, 400 and 1,000 nm. Such misinterpretation was not made when using NTA, since the presence of different populations was clearly detected by sample visualization. Although the big spike made the NTA measurements considerably more complex, all the beads in the sample could still be visualized and accurately sized (Fig.
As already mentioned, NTA involves several adjustment steps during the video capture and analysis, which are essential to obtain accurate measurement results. The power of choice given to the operator may be seen as a great advantage, but also raises concerns. The operator can easily choose settings that ignore or emphasize the presence of certain particles, which makes the veracity of the results dependent on individual judgment and experience.
To obtain accurate results, one should (i) thoroughly search with the microscope for the presence of all particle size classes in the sample, (ii) optimize the video settings in order to capture all these identified particle sizes, and (iii) adjust the analytical settings to unbiasedly track all moving particles captured by the video. With the purpose of clarifying the weight of the different software settings on the result, the influence of each parameter was carefully analyzed, as summarized in Table
Since the quality of NTA data will be dependent on the software settings used, which in turn depend on sample properties, as well as on the experience and decisions of the operator, NTA will be very difficult to qualify as a quality control method. Instead, NTA is—like DLS—very useful as a characterization tool, as will be demonstrated in the applications discussed below.
In order to evaluate the analytical performance of NTA for nanoparticles commonly used in the pharmaceutical field, PLGA particles, TMC particles and liposomes were analyzed with NTA and the results compared to DLS (Fig. Drug delivery nanoparticles measured with NTA and DLS. The size distribution (
DLS analysis resulted in a Z-ave of 411 nm and a PdI of 0.09 for the TMC particles, indicating a relatively monodisperse sample. This was confirmed by the visualization of the particles in the NanoSight sample chamber (Fig.
The PLGA particles analyzed by DLS exhibited a Z-ave of 308 nm and a PdI of 0.22. This PdI value suggests that the PLGA particles were more polydisperse than the TMC particles. The relatively high polydispersity of the PLGA particles became very clear during the visualization of the sample by NTA (Fig.
DLS analysis of the liposomes resulted in a Z-ave of 117 nm and a PdI of 0.248, suggesting that this sample was more polydisperse than the PLGA sample. Surprisingly, the visualization of the liposomes with NTA revealed a fairly monodisperse sample, and the standard deviation obtained was 77 nm, which is even smaller than that of TMC particles. The mean size value obtained with NTA was 154 nm, which is again larger than the Z-ave given by DLS. This time the peak given by DLS is shifted to smaller sizes as compared to the one obtained by NTA. Given that DLS has a lower detection limit than NTA, it is possible that smaller particles (<30 nm) present in the formulation decreased the Z-ave in DLS, which would also explain the relatively high PdI. Other analytical techniques would have been necessary to clarify this observation.
A heat-stressed IgG formulation and a metal-oxidized insulin formulation were used to evaluate the analytical performance of NTA with protein aggregates (Fig. IgG aggregates (obtained by heat stress) and insulin aggregates (obtained by metal catalyzed oxidation) measured with NTA and DLS. The size distribution (
The visualization of the aggregated IgG sample with NTA allowed the distinction of two or more light scattering centers in very large aggregates (>1 μm), which suggests that they were formed by the assembly of smaller aggregates. However, the brightness of such large particles interferes with the optimization of the instrument settings, because it makes smaller aggregates more difficult to detect. The monomer present in the sample increases the background light and also makes smaller aggregates more difficult do detect.
In the aggregated insulin sample, the native insulin was only detected by DLS and found to have an average size of about 6 nm, consistent with literature data for insulin at neutral pH (
Several factors are known to induce protein aggregation, and some characterization techniques have been reported to induce or disturb the aggregation state (
Overall, DLS sample treatment seems to be less aggressive than NTA, which is an advantage for unstable samples, such as protein aggregates. However, the high peak resolution and suitability for polydisperse samples make NTA a very useful technique to analyze protein aggregates.
The heating block of NanoSight allows NTA measurements at temperatures ranging from room temperature to 50°C, which enables the live monitoring of protein aggregation at elevated temperatures. This feature is also possible with DLS, but the possibility of visualizing the aggregates being formed with NTA gives a more complete overview of the aggregation process.
An IgG formulation was heated at 50°C for 45 min in the NanoSight heating block, while movies were being recorded (Fig. Live monitoring of IgG aggregation at 50°C in the NanoSight sample chamber. The size distribution (
In this work, we evaluated NTA as a new characterization method for nanoparticle analysis and compared it to DLS. The differences between the two techniques are summed in Table Comparison of NTA with DLSDLS NTA Characteristics Size accuracy Accurate for monodisperse samples, inaccurate for polydisperse samples Accurate for both monodisperse and polydisperse samples Peak resolution Low (>3 fold difference in diameter) High (<0.5 fold difference in diameter) Size range Ca. 1–1,000 nm Ca. 30–1,000 nm Concentration range (particles/ml) Broad (about 108–1012) Limited (107–109) Population ratio Narrow range Very broad range Large influence on size accuracy and distribution Very little influence on size accuracy and little influence on size distribution Reproducibility More reproducible Less reproducible Contaminations Large particles can seriously compromise the results Dust, microorganisms or aggregates easily detected, large particles have little influence Operational Device handling Very user-friendly Requires several parameter adjustments Little sample handling Sample handling may affect size distribution Possibility of using disposable cuvettes Sample chamber must be cleaned after each sample Experienced operator required Time consumption Between 2 to 5 min per measurement Between 5 min to 1 hour per measurement Particular output Approximate size distribution Individual particle sizing Intensity distribution Number distribution Z-ave Approximate concentrations PdI Individual particle intensity Sample visualization No Yes Applications Drug delivery nanoparticles Accurate sizing Accurate sizing Approximate size distribution More reliable size distribution Hard detection of contaminants Easy detection of contaminants Protein aggregates Approximate size distribution Accurate size distribution Includes protein monomers and some excipients Protein monomer and small aggregates excluded Presence of very large aggregates has a big impact on the result Aggregate architecture information Interference of large aggregates may sometimes be overcome
NTA proved to be very suitable for analyzing drug delivery nanoparticles. This technique is also very suitable for analyzing protein aggregates, but care should be taken that sample preparation does not influence the aggregate distribution in the measurement cell. It also gives the possibility of live monitoring heat-induced aggregation, providing information about the aggregation kinetics.
This research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs. The authors are grateful to Bram Slütter and Ana Silva for the preparation of the TMC nanoparticles and PLGA particles, to Myrra Carstens for the preparation of the liposomes and to Riccardo Torosantucci and Olubukayo Oyetayo for preparing the aggregated protein samples.