Filling Adeno-Associated Virus Capsids: Estimating Success by Cryo-Electron Microscopy

Abstract

Adeno-associated viruses (AAVs) have been employed successfully as gene therapy vectors in treating various genetic diseases for almost two decades. However, transgene packaging is usually imperfect, and developing a rapid and accurate method for measuring the proportion of DNA encapsidation is an important step for improving the downstream process of large scale vector production. In this study, we used two-dimensional class averages and three-dimensional classes, intermediate outputs in the single particle cryo-electron microscopy (cryo-EM) image reconstruction pipeline, to determine the proportion of DNA-packaged and empty capsid populations. Two different preparations of AAV3 were analyzed to estimate the minimum number of particles required to be sampled by cryo-EM in order for robust calculation of the proportion of the full versus empty capsids in any given sample. Cost analysis applied to the minimum amount of data required for a valid ratio suggests that cryo-EM is an effective approach to analyze vector preparations.

Introduction

Parvoviruses are small 22–25 nm in diameter icosahedral nonenveloped viruses that package a single-stranded DNA genome of ∼5 kb and infect a wide variety of organisms ranging from insects to humans. Previous studies have shown that empty capsids form first and decrease in number over time as genome-filled capsids appear.¹ Recombinant expression of the major capsid proteins often yields the self-assembly of virus-like particles, such as for minute virus of mice (Protoparvovirus), parvovirus B19 (Erythroparvovirus), adeno-associated virus (AAV) (Dependoparvovirus), human bocavirus 1–4 (Bocaparvovirus), and Aleutian mink disease parvovirus (Amdoparvovirus), suggesting that capsid assembly is independent of the presence of genome.^2–7 Together with identification of genome packaging motors, the genome is most likely driven into preformed parvovirus capsids, which leads to mixed populations of genome-filled and empty capsids.^8–10

AAV has been developed as a successful gene therapy vector. Notably, the recent approval of its use in gene therapy for acute lymphoblastic leukemia, B cell lymphoma, and retinal dystrophy has led to increased large scale propagation and purification of AAV vectors and on-going efforts to improve yield.^11–14 However, the persistence of unpackaged virions remains a major challenge for efficient AAV vector production.¹⁵ Empty capsids pose several liabilities, such as inhibiting the transduction by competing with capable genome-filled vectors for host cell attachment and entry as well as promoting an immune response without the therapeutic benefit of delivering the payload.^16–19

A variety of techniques are currently employed to remove empty particles from purified vector preparations, including density gradient ultracentrifugation and ion-exchange chromatography.^20–24 An accurate method of assessing the proportion of empty particles in any purified preparation is imperative for determining sample quality and estimating efficacy.²⁵ Optical density measurements, quantitative PCR (qPCR) and negative stain transmission electron microscopy (TEM) are used routinely for quantification of vectors, but tend to be imprecise since background contributes to signal and negative stain can alter the state of the particle and cause damage to the capsid in some cases.^26–29 In this article we propose using cryo-electron microscopy (cryo-EM) to see particles in native or near-native states to ensure accuracy of the capsid states, and to allow straightforward estimation of DNA-filled and empty capsid populations.

For cryo-EM, biological molecules are captured in a hydrated state by plunge freezing before data are collected using an electron microscope. The resulting images have a low signal-to-noise ratio due to the lack of contrast enhancers such as stains and a limited electron dose to avoid specimen damage. Single particle reconstruction methods have been developed to enhance the signal by iteratively sorting and averaging particle images, and now most three-dimensional (3D) reconstruction programs group and align particles corresponding to the same orientations in a process called two-dimensional (2D) classification.^30,31 The general approach involves a multireference refinement process in which particle images are compared against reference images in different orientations. Probability scores are assigned for each comparison according to the degree of similarity with the reference image. Class averages are generated as weighted averages of all assignments, which are then used as new reference images for each successive iteration.^32–35 2D classification is usually followed by 3D classification, which is not unlike the former, except a user-defined reference model is used as a template to sort particles into different classes.³⁶ With a volume reference, this process is capable of distinguishing samples with structural heterogeneity such as conformational variabilities in protein complexes and virus particles with varying genomic contents, multiple structural states, or polymorphism.^37–41

In this article we sorted from a mixed population of genome-filled and empty AAV particles to solve the corresponding atomic resolution structures. The DNA-filled capsids yielded a density map at 3.42 Å that included significant density consistent with packaged DNA, whereas those images identified as empty capsids yielded a structure at 3.26 Å that was indeed lacking internal density resolution. We then used the images to develop a reliable classification method that showed statistically the minimum number of particles needed to complete the analysis. Our method readily distinguished genome-filled, partially filled, and unfilled populations to determine the proportion of full to empty capsids as a tool for gene therapy research. Cost analysis demonstrated that this is an affordable method for researchers and clinicians.

Materials and Methods

AAV3 propagation and purification

Large-scale polyethylenimine transfections of AAV cis, AAV trans, and adenovirus helper plasmids were performed in a 10-layer hyperflask (Corning) with near-confluent monolayers of HEK293 cells. Plasmids were transfected at a ratio of 2:1:1 (260 μg of adenovirus helper plasmid/130 μg of cis plasmid/130 μg of trans plasmid). PEI Max (Polysciences)/DNA ratio was maintained at 1.375:1 (w/w). Downstream purification of sample 1 was performed by affinity chromatography using AVB sepharose HP (25-4112-11; GE Healthcare) as previously described.⁴² Sample 2 was purified by tangential flow filtration and ultracentrifugation as previously described.¹³ DNase-I-resistant vector genome copies were used to titrate AAV preparations by TaqMan qPCR amplification (Applied Biosystems 7500; Life Technologies) with primers and probes detecting promoter, transgene, or polyadenylation signal coding regions of the transgene cassette. The purity of the large-scale preparations was evaluated by sodium dodecyl sulfate polyacrylamide gel electrophoresis.

Titan Krios data collection (sample 1)

Three microliters of the sample was applied to freshly glow-discharged Quantifoil R2/1 grids (Quantifoil Micro Tools; GmbH, Jena, Germany), which were then blotted and vitrified in liquid ethane using an FEI Vitrobot Mark IV (Thermo Fisher Scientific, Waltham, MA). Images were acquired on the Thermo Fisher Titan Krios G3 microscope operated with an accelerating voltage of 300 kV. An “Atlas” image was assembled from micrographs taken at 165 × magnification in linear mode on a Thermo Fisher Falcon 3ec direct electron detector, and suitable areas were selected for data collection. Automated data collection was set up using Thermo Fisher's EPU software. Images were collected on the Falcon 3ec in counting mode using a nominal magnification of 59,000 × , resulting in a calibrated pixel size of 1.136 Å at the sample. The microscope was operated with a 70 μm condenser aperture and a 100 μm objective aperture. Four nonoverlapping exposures per 2-μm-diameter hole were acquired with the beam in parallel mode. Total dose per exposure was set at 45 e^–/Ų.

Polara data collection (sample 2)

Grid preparation and data collection on an FEI Polara G2 microscope (Thermo Fisher Scientific) were carried out similarly to that already described for the Krios. The Polara was operated at 300 kV and a nominal magnification of 115,0000 × with defocus values ranging from −1.5 to −4.0 μm. Images were collected under the software control of Thermo Fisher's EPU program using an FEI Falcon 2 direct electron detector with postcolumn magnification of 1.4 × , yielding a calibrated pixel size at the sample of 0.93 Å. The microscope was operated with a 70 μm condenser aperture and no objective aperture.

Data processing

For both data sets, all movie frames were aligned using Motioncor2 program using 5 by 5 patching.⁴³ Contrast transfer function estimation was performed using Gctf program.⁴⁴ Particle picking was performed by RELION autopicking using the 2D class averages of ∼1,000 manually picked particles as templates.⁴⁵ A total of 132,631 particles were picked from the Titan Krios data set and 169,181 particles were picked from the Polara data set. The random subsets of particles were created using the “shuffle” command in LINUX from the total “particles.star” and these were processed using the 2D classification programs in RELION 2.1 and cryoSPARC.^46,47 Proportions of full and empty particles were calculated from 3D classification analysis from RELION 2.1, without imposing symmetry (C1).

Image processing, fitting, radial analysis, and density histograms

The maps and fittings were rendered in UCSF Chimera and model building was performed with Coot.^48,49 The models were refined against the density maps using PHENIX “real space refine” and the refined structures were validated using MolProbity.^50,51 Local resolution estimations were performed using RELION. Density along the radius of each 2D class average was calculated using the “bradial” program of the BSOFT software.⁵² A step size of 2 was used and hence the density at every 2.272 Å (twice the pixel size) of sample 1 and every 1.86 Å of sample 2 was plotted. The background subtraction option was used to reduce any background noise from the 2D class averages. Internal density histograms were generated using PYTHON programming to visualize the frequency of each DNA density value that corresponded to the distance 0–75 Å. Based on the peaks displayed in the histograms, the density range for empty, intermediate, and full populations was defined as 0–0.5, 0.5–1, and 1–1.5, respectively.

Statistical analyses

We used the percentage of full particles in random subsamples as the response variable in a least squares linear model (JMP 13.1; SAS Institutes) to determine how the estimate varied with sample, the number of particles examined, construction program, and sample by construction program interaction. Least squares means from this model, representing estimates at the mean subsample size (N particles = 8,310), were used to estimate effect sizes.

Analytical ultracentrifugation

The vector samples were analyzed in 1 × phosphate-buffered saline (PBS)–0.001% Pluronic F68. The ultracentrifugation was performed using cells containing two-sector assemblies in an eight-chamber An50-Ti rotor spinning at 11,500 g (12,000 rpm) in a Beckman Coulter Proteome Lab XL-1 ultracentrifuge. One sector of the cell contained 430 μL 1 × PBS–0.001% Pluronic F68 reference solution; the other sector contained 400 μL of vector in 1 × PBS–0.001% Pluronic F68 with an absorbance at 280 nm of 0.2–0.8. The scan data were recorded at 280 nm.

The scan data were fit to a Continuous c(s) Distribution model using SedFit with regularization by second derivative over a range of 0–500s, a frictional ratio of 1.0, and an F-ratio confidence level of 0.95.⁵³ The peak identity was determined based on the method of Burnham et al.⁵⁴ The molar quantity of each species was determined by calculating the respective molar extinction coefficients. For empty particles, the ɛ280_(capsid) was calculated to be 6,337,200/M cm.⁵⁵ For full particles, the ɛ280_(vector) was calculated from the equation hereunder, which was modified from Sommer et al. to reflect differences in dsDNA versus ssDNA.²⁶

$$\epsilon 280_{\left(vector\right)}=15.9\times M{W}_{DNA}+\epsilon 280_{\left(capsid\right)}.$$

The continuous distribution plots revealed the presence of a peak with a sedimentation coefficient intermediate to that of the empty (E) and full (F) particles. Burnham et al. demonstrated that this corresponds to particles with smaller DNA fragments packaged in the capsid.⁵⁴

Data availability

Cryo-EM maps were deposited into the EM data bank under accession numbers EMD-20624 (empty) and EMD-20625 (full).

Results

Packaged and unpackaged AAV3 capsids are structurally indistinguishable

Distinct populations of genome-filled and genome-unfilled AAV3 capsids within the same cryo-EM data set were reconstructed independently to 3.42 and 3.26 Å, respectively (Fig. 1A). The AAV3B X-ray crystal structure (PDB ID: 3KIC) was used as the initial template and the model building proceeded with amino acid differences corrected.⁵⁶ The two cryo-EM structures were superimposable with the root mean square deviation value of 0.244 Å, indicating that the full and empty capsid structures are essentially identical (Fig. 1B). We were able to trace the majority of the alpha-carbon backbone in both maps with the exception of density missing at the threefold spikes on the exterior of the capsid (amino acids 454–457) and at the fivefold pore (amino acids 326–329). However, the central sections of the genome-filled and empty maps show difference in density, indicating presence and absence of genome (Fig. 1C). Local resolution mapping showed most of both capsid shells were resolved to 3.2 Å but the spikes and fivefold pore were between 3.7 and 4 Å resolution (Fig. 1D), indicating those regions to be of poorer density likely resulting from flexibility and both maps reported essentially equal B factors, suggesting flexibility was similar between the two structures.

Figure 1.

Cryo-EM density maps of AAV3 genome-filled (left) and empty (right) capsids. (A) Surface renditions of the maps colored by radius to highlight surface features—color key indicates distance from the center of the capsid. (B) AAV3 full (blue) and empty (red) builds superimposed to show near identical structures. (C) Central cross-sections illustrate the quality of the density maps and the presence (left) or absence (right) of genome density. (D) Local resolution estimation reveals flexibility at the threefold region—color key indicates resolution. cryo-EM, cryo-electron microscopy.

A general model to estimate proportion of genome-filled capsids at a defined level of confidence

Since the presence or absence of genome was revealed by the central section of each map (Fig. 1C), which is essentially a 2D slice of the 3D map, we were interested in exploring the role of 2D class averages in differentiating particles containing genome and empty particles. The 2D classification process, an intermediate step in the 3D reconstruction approach, successfully grouped particles by the presence or absence of packaged DNA density and hence resulted in two types of 2D class averages (Fig. 2B). Genome-containing particles had density throughout the entire region of the projected capsid, whereas the class averages containing empty particles were represented by densities that appear ring-like with empty centers due to the absence of genome. Radial density profiles (Fig. 2C) along with the internal density histogram (Fig. 2D) confirmed this visualization of two distinct populations: one where the density values ranged between 1 and 1.5 from the center to the edge of the particle corresponding to DNA-filled capsids, and another where the density values increased sharply from the center to the edge, ranging between 0 and 0.5 and peaking at 1.5, indicating the presence of an empty core.

Figure 2.

(A) Schematic figure represents internal DNA density (light blue), capsid density (blue), mask used in 2D classification (black), and box used to extract particles from micrographs (gray) and the distance from the center to the edge of the DNA density (red) and edge of capsid density (yellow). (B) 2D class averages reveal classes of genome-filled (solid) and empty (hollow) capsids. (C) Radial density profiles of the class averages reveal straightforward separation into two distinct populations: One with density remaining uniform radially, representing full population and another with sharp increase in density from the center to the edge of the capsid representing empty capsids. (D) Internal density histogram displays two peaks representative of the empty and full populations.

To simulate the variability in proportion of genome-filled particles that occurs in large-scale AAV preparations, another AAV3 sample (sample 2), which was purified using a different method, was added to the analysis (Supplementary Fig. S1A–C). Both AAV3 data sets (samples 1 and 2) contained a little >100,000 particles, sufficient for solving near-atomic resolution structures but well in excess of the needs for generating the 2D and 3D classes that allow measuring capsid populations. Hence, to assess a minimum particle count for such measurements, we selected five random subsets of particles ranging in number from 1,000 to 20,000. A minimum of 1,000 particles were determined empirically, since fewer particles produced classes that were poorly centered and noisy, making it more difficult to assign full and empty (Supplementary Fig. S2). We performed 2D classification for each subset of particles using two different software packages, RELION 2.1 and cryoSPARC, to test for bias in proportion estimation between software programs.

Statistical analysis of subsamples (Fig. 3) showed that the estimated percentage of full particles was unrelated to sampling effort (P = 0.27), differed between the two samples as already described (mean percentage full = 23 vs. 62% in these samples, P < 0.0001), and varied only slightly between the two classification programs (mean % full particles = 44 in RELION vs. 41 in cryoSPARC sampled; P < 0.0001) (Supplementary Table S1). There was no interaction between sample and program (P = 0.39). Variance in the estimated percentage of full particles stabilized when the total number of particles sampled exceeded 5,000 to 10,000 (Fig. 3A) for both samples. Program-wise comparison of percentage full particles also showed the same effect (Fig. 3B)

Figure 3.

(A) Percentage of full virus particles as a function of the number of particles in subsamples of a larger data collection. Data points from sample 1 are represented in blue and sample 2 in red. Dotted lines connect the upper and lower 95% confidence interval for the mean at each targeted number of particles in the subsamples. Results from both analysis programs are pooled in this plot. (B) Box-plot compares RELION and cryoSPARC analyses of the two samples. Bottom, middle, and top line of the box represents first quartile, median, and third quartile, respectively, and bottom and top whiskers represent the maximum and minimum value, respectively.

To generalize the relationship between the virus sample characteristics and the minimum number of particles needed to confidently characterize it, we modeled the state of a capsid as a random variable (X) that can either be functional (full) or nonfunctional (empty and intermediates) (Supplementary Data). According to the resulting model, the maximum number of particles “n” required for a given confidence level “C” and tolerance level “m” was derived [Supplementary Eq. (7)] whose commonly used values are given in Table 1. For example, if the required confidence level is 95% (C = 0.95) and tolerance level = 1% (m = 0.01), by using

Table 1.

Value of “n” shows the number of particles that need to be analyzed with for a given confidence interval (C) and tolerance (m) to estimate the proportion of genome-filled AAVs in any vector preparation with a binary particle model (functional vs. nonfunctional)

C	m
	0.1%	0.20%	0.50%	0.75%	1.0%	1.5%	2%
95%	960,361	240,087	38,411	17,069	9,600	4,264	2,397
96%	1,054,467	263,614	42,175	18,742	10,540	4,682	2,632
97%	1,177,318	294,326	47,088	20,925	11,769	5,228	2,939
98%	1,352,968	338,238	54,114	24,047	13,524	6,008	3,377
99%	1,658,718	414,674	66,342	29,482	16,581	7,365	4,140
99.9%	2,706,881	676,712	108,265	48,112	27,058	12,020	6,756
99.99%	3,784,161	946,029	151,352	67,259	37,827	16,803	9,445

AAVs, adeno-associated viruses.

$$n=\frac{{\left(er{f}^{-1}\left(C\right)\right)}^2\left(1-4m^2\right)}{2m^2}$$

“n” can be calculated as 9,600. Hence, upon collecting data on 9,600 particles and sorting them into 2D classes, the researcher can be at least 95% certain that the proportion in the original preparation (p) is within 1% range of the calculated sample proportion.

Determining the proportion of full versus empty capsids by 3D classification of cryo-EM images

Although the 2D class averages of sample 1 separated into distinct populations (Fig. 2B), the genome-filled population of sample 2 had a broader distribution of density (Supplementary Fig. S1B). Hence, we proceeded to perform 3D classification analysis with a reference model to better separate the particles according to their genome density. The 3D classification was performed without imposing any symmetry (C1), as partial genome density is classed with full particles upon icosahedral symmetry averaging. The resulting 3D classes (Supplementary Fig. S3) revealed the presence of empty, full, and intermediate genome density populations. Sample 1 contained 19.96% full capsids and 80.04% empty capsids (combination of unfilled and partially filled) and sample 2 contained 41.24% full and 58.76% empty capsids. Analytical ultracentrifugation was used as an independent method to cross-correlate the findings of cryo-EM and the results are reported in Table 2. Thus we conclude that 3D classification of cryo-EM images is well suited to measuring the proportion of genome-filled and empty capsids and will detect differences resulting from different preparation methods.

Table 2.

Comparison of proportion of full and empty capsids as evaluated by cryo-EM 3D classification and AUC

Analysis	Sample 1 (%)		Sample 2 (%)
	Full	Empty	Full	Empty
3D classification	19.96	80.04	41.24	58.76
Analytical ultracentrifugation	12.66	87.34	42.41	57.59

3D, three-dimensional; AUC, analytical ultracentrifugation; cryo-EM, cryo-electron microscopy.

Time and cost analyses reveal cryo-EM as a viable option for determining proportion of genome-filled and empty capsids

For practical purposes, 99% confidence with 1% tolerance is considered for which 16,851 particles need to be sampled (Table 3). From our previous data collections, at 59,000 × magnification, at a pixel size of 1.136 Å, we were able to collect data with an average of 150 particles per micrograph (Supplementary Fig. S4) when a purified AAV sample of 2 mg/mL concentration is applied to a Quantifoil R2/1 grid coated with a thin film of continuous carbon. Under these conditions, recording 150–200 micrographs was sufficient to collect ∼20,000 particles. The total time required, including vitrifying grids, data collection, and data processing with RELION 3.0, is represented in Fig. 4.⁵⁷ Motion correction for 157 micrographs with Motioncorr2 program took 1.5 h, from which a total of 25,517 particles were picked using RELION autopicking function. The total data processing time was <4 h with two 2.3 GHz Intel Xeon CPUs combining 64 cores, 384 GBs memory, 2TBs of SSD scratch space, and two NVIDIA Titan Xp graphics processing units (GPUs). The total costs for sample screening, data collection, and data processing are reported in Table 3. Data collection costs vary from instrument to instrument, but the values reported are representative.

Table 3.

Costs in USD to prepare, image, and analyze a sample of appropriate purity and concentration

	Internal Users	External Academic	Industry
Consumablesa	48.50	77.00	80.00
Vitrification	60.00	122.29	140.00
Cryo-EM data collection (4 h)	338.32	540.80	800.00
Staff time	275.04	439.68	480.00
4 h processing	200.00	400.00	460.00
Total	921.86	1579.77	1960.00

^aIncludes c-clip/ring, grids, storage box.

Figure 4.

Breakdown of time involved in grid preparation, imaging, and data processing for 25,517 particles that are sufficient for determining a robust estimate of the proportion of particle populations.

Discussion

Quantification of AAV vectors are currently being performed by enzyme-linked immunosorbent assay (ELISA), qPCR, optical density measurement, charge detection mass spectrometry (CDMS), sedimentation velocity analytical ultracentrifugation (SV-AUC), negative stain TEM, etc., with negative stain TEM being the only visual determination.^{26–29,54,58–60} The optical density method determines the nucleic acid-to-protein ratio in the preparation by measuring the absorbance of the sample at 260 and 280 nm. The absorption ratio, A₂₆₀/A₂₈₀, varies depending on the proportion of full to empty particles and is often verified by ELISA, which is used to quantify the total number of capsids, and qPCR, which is used to detect the genome levels. One of the major drawbacks of this method is that the presence of protein and nucleic acid impurities in the sample could result in incorrect estimations, in addition to requiring high concentrations of virus capsids. Both CDMS and SV-AUC are analytical methods capable of quantitatively assessing empty, partially filled, and full populations. However, these methods have not been used widely due to the fact that they require significant amount of sample, are time consuming, are not widely available, and do not lend themselves easily to higher throughput analysis.

One of the major advantages of cryo-EM compared with the other techniques already mentioned is that impurities and other background protein do not interfere with the quantification because they are eliminated during data processing. For example, protein contaminants or disintegrated capsid proteins can contribute to signal in optical density measurement or ELISA, depending upon the antibody used, but any cryo-EM 2D class average that does not represent stable AAV particles is eliminated. Although there is a possibility that noise may be introduced during automated particle picking by erroneous selection of background, these wrongly selected areas are eliminated by user supervision, which includes particle sorting and using manually picked particles as an initial template.⁶¹ In negative stain TEM, there is a possibility of the heavy metal stain penetrating the empty capsids, which makes them indistinguishable from the genome-filled capsids. In addition, capsids may be disrupted by dehydration in the microscope vacuum, neither of which occur in cryo-EM.

Our analysis combined reference-free 2D classification to calculate the minimum number of particles required for proportion calculation and 3D classification to quantify DNA density. 2D classification served well to perform initial classification of vectors into genome containing and empty particles (Fig. 2B–D), and illustrated that variance decreased with increase in number of particles irrespective of sample proportion (Fig. 3A, B). However, 3D classification analysis was required to assign functional or nonfunctional status of vector without ambiguity. By proceeding to 3D classification, we were able to thoroughly analyze not only the presence or absence of DNA, but also particles whose density lied in between that of full and empty population (Supplementary Fig. S3), which indicates the presence of a population of capsids that packaged partial genome, which has been previously observed with other methods as well.^54,58

Since 3D classification requires a 3D reference model, one might be concerned about reference bias. But programs such as RELION employ low-resolution references, low pass filtered to ∼60 Å resolution, which are devoid of detailed structural information to avoid reference bias. Another issue might be the overestimation of a majority population in comparison with the minority population, especially for our purpose, where vector preparations are subjected to a variety of separation processes to enrich for full AAV. Although the sorting of heterogeneous particles into their respective homogeneous classes is dependent on the extent of difference between the populations, overestimation can be minimized by resorting misclassified particles with iterative rounds of 3D classification.⁶² Moreover, 3D classification has proven to be a robust and sensitive scheme as it is capable of classifying heterogeneous populations <5% and even as low as 0.8% of total particles in some cases.^63,64

New cryo-grid preparation techniques may further enhance the throughput and cost-effectiveness of using cryo-EM and 2D classification to measure capsid populations. One example is the Spotiton plunge-freezer that allows more than one sample to be vitrified on the same grid, reducing the cost and time for grid preparation, the cost of materials including sample and grids, and saving microscope time used for switching grids.⁶⁵ Developments in analysis algorithms moving toward decreasing user supervision and processing time, computational hardware including GPU-based calculations, and access through cloud computation services will further increase efficiency as well as availability of this technique for new classes of users.^47,66 Owing to the broad applicability of this method, it can be used with other viral vectors such as adenoviruses or lentiviruses to visualize effective transgene packaging and in case of lipid nanoparticles that are used to transport drugs.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Disclosure

No competing financial interests exist.

Funding Information

This study was supported, in part, by Pennsylvania Department of Health CURE funds. Research reported in this publication was also supported by the Office of the Director, National Institutes of Health, under Award No. S10OD019995.

Supplementary Material

Supplementary Data

Supplementary Table S1

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

Supplementary Figure S4

Supplementary Figure S5

Supplementary Figure S6

Supplementary Figure S7