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Scientific RepoRts | 6:29132 | DOI: 10.1038/srep29132
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VirusMapper: open-source 
nanoscale mapping of viral 
architecture through super-
resolution microscopy
Robert D. M. Gray1,2,*, Corina Beerli1,*, Pedro Matos Pereira1,3, Kathrin Maria Scherer1, 
Jerzy Samolej1, Christopher Karl Ernst Bleck4, Jason Mercer1 & Ricardo Henriques1,3
The nanoscale molecular assembly of mammalian viruses during their infectious life cycle remains 
poorly understood. Their small dimensions, generally bellow the 300nm diffraction limit of light 
microscopes, has limited most imaging studies to electron microscopy. The recent development of 
super-resolution (SR) light microscopy now allows the visualisation of viral structures at resolutions 
of tens of nanometers. In addition, these techniques provide the added benefit of molecular specific 
labelling and the capacity to investigate viral structural dynamics using live-cell microscopy. However, 
there is a lack of robust analytical tools that allow for precise mapping of viral structure within the 
setting of infection. Here we present an open-source analytical framework that combines super-
resolution imaging and naïve single-particle analysis to generate unbiased molecular models. This 
tool, VirusMapper, is a high-throughput, user-friendly, ImageJ-based software package allowing for 
automatic statistical mapping of conserved multi-molecular structures, such as viral substructures or 
intact viruses. We demonstrate the usability of VirusMapper by applying it to SIM and STED images of 
vaccinia virus in isolation and when engaged with host cells. VirusMapper allows for the generation of 
accurate, high-content, molecular specific virion models and detection of nanoscale changes in viral 
architecture.
Probing viral structure often provides novel insight into the structure-function relationship of protein locali-
zation within viral particles1,2. This information is key to understanding viral organization, and ultimately the 
mechanisms of virus metastability that dictate virus assembly and disassembly in host cells3. Due to their size the 
methods of choice for investigating the nanoarchitecture of viruses are electron microscopy (EM) and electron 
tomography (ET). Taking advantage of the stability of cell free viruses, single particle analysis (SPA) is often 
employed in EM and ET to achieve the resolution required to study viral architecture. Nonetheless, limitations 
with regard to combining these techniques with molecular specific labelling make it difficult to interrogate the 
location and identity of specific molecules within virus particles in a quick and reliable way. Super-resolution 
(SR) microscopy is a relatively new technique that provides the capacity to resolve molecular assemblies at scales 
considerably lower than the diffraction limit (~300 nm), while simultaneously allowing for the visualization of 
specific proteins within complex nanostructures. The recent application of SPA to SR microscopy data sets has 
proven particularly powerful for precision mapping of individual proteins within nanoscale molecular assem-
blies, including viruses4–7. However, the promise of combining SPA and SR microscopy is challenged by the lack 
of freely available easy-to-use compatible software packages. Here we describe an ImageJ and Fiji-compatible, 
high-throughput SPA framework that allows for automated detection, segmentation, alignment, classification 
and averaging of thousands of individual viruses for the generation of SR models of virus nanoarchitecture 
1MRC-Laboratory for Molecular Cell Biology. University College London, Gower Street, London, WC1E 6BT, United 
Kingdom. 2Centre for Mathematics and Physics in Life Sciences and Experimental Biology (CoMPLEX), University 
College London, Gower Street, London, WC1E 6BT, United Kingdom. 3Department of Cell and Developmental 
Biology, University College London, Gower Street, London, WC1E 6BT, United Kingdom. 4Biozentrum, University 
of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland. *These authors contributed equally to this work. 
Correspondence and requests for materials should be addressed to J.M. (email: jason.mercer@ucl.ac.uk) or R.H. 
(email: r.henriques@ucl.ac.uk)
received: 11 February 2016
accepted: 15 June 2016
Published: 04 July 2016
OPEN

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(Supporting Information Software). We have demonstrated the method with structured illumination microscopy 
(SIM) and stimulated emission depletion (STED) microscopy and the method is also expected to work for high 
quality single-molecule localization microscopy (SMLM) data, as long as the labelling and localisation density 
is high enough. Caution is advised for sparsely labelled data where our alignment method may no longer func-
tion correctly. VirusMapper is an open-source, user-friendly software that provides researchers with a powerful 
tool to create accurate, detailed virus models thereby opening new doors to further our understanding of virus 
structure-function relationships.
Results
VirusMapper workflow for model-based analysis of virus architecture. 
To determine the structural 
model generation ability of VirusMapper we applied it to the prototypic poxvirus, vaccinia virus (VACV)8. The 
major infectious form of VACV, mature virions (MVs), are brick shaped particles measuring 360 ×  270 ×  250 nm9. 
The MVs are composed of ~80 different proteins8,10 divided between 3 viral substructures discernable by EM: 
a dumbbell shaped core that contains the DNA genome, two proteinaceous structures termed lateral bodies 
(LBs) that sit within the core concavities, and a single lipid bilayer membrane that encompasses these structures 
(Fig. 1a,b). EM and ET have provided information about the general structure of VACV11. Biochemical frac-
tionation studies have been used to differentiate viral membrane from core proteins12,13, and immuno-EM has 
provided the localization of a small subset of proteins within virions14–16.
The well-known structural organization, and the ability to specifically visualize multiple virion proteins or 
components simultaneously makes VACV MVs an excellent system to showcase the potential of VirusMapper. To 
generate multi-color SR images, purified viruses were either bound to coverslips or exposed to cells and imaged 
by either SIM or STED.
The combination of VirusMapper and super-resolution provides several advantages: i) for VACV it provides the 
capacity to separate virion substructures (Fig. 1c); ii) it allows the use of a combination of labelling strategies; iii)  
easy acquisition of multi-color SR images; and iv) hundreds to thousands of viruses can be imaged within a single 
field of view with minimal optical distortion.
With SIM imaging we acquired images of a recombinant virus that packages a mCherry-tagged version of 
the outer core protein A4. We reasoned that the virus core was the ideal structural element to initiate mapping as 
its near ellipsoid shape offered the possibility of determining particle orientation. After acquisition, images were 
data mined by VirusMapper in three main stages: segmentation, seed selection, and model generation (Fig. 2 and 
Supplementary Fig. 1). For segmentation a particle detection algorithm automatically finds and realigns viral parti-
cles based on the oblong appearance of A4 (Fig. 2a, I). From these, an image sequence of individual virions oriented 
with their long axis in the vertical position is generated (Fig. 2a, II). Next, a small subset of representative virus 
core images is selected based on a priori knowledge and highlighted as seeds for template matching (Fig. 2a, III).  
Following seed selection, each individual viral particle detected and segmented from the SIM images is regis-
tered in its rotation and coordinates against the given seed and sorted by normalized cross-correlation similarity 
(Fig. 2a, IV and Supplementary Note 1). For model generation, the registered core images are averaged using the 
normalized cross-correlation similarity factor as weights, consequently a Mean Square Error (MSE) of the model 
against individual elements is also calculated. The process of alignment and template matching is iterated using 
each newly generated model as a seed, minimizing the MSE of the model (Fig. 2a, V and IV).
Multi-seed mapping for virus orientation assessment. 
Having established a model of the viral core by 
mapping A4, we next labelled multiple virus proteins in combination to map their locations within viral substructures.  
We collected SIM images of recombinant virions that package fluorescently tagged versions of the inner core pro-
tein L4 and the major LB constituent F17. As before, collected images were processed by VirusMapper. Analysis 
is initiated with virion segmentation and realignment for both channels in parallel (Fig. 2b, I). For multi-color 
model generation in VirusMapper a reference channel is selected, in this instance A4, on which the registration 
estimation is based and then applied to all channels (Fig. 2b, III). By selecting multiple A4 seeds to probe the 
Figure 1. VACV structural organization. (a) Scanning (left) and transmission (right) EM of a VACV mature 
virion seen in frontal view and sagittal cross-section respectively. (b) Structural illustrations of MVs in sagittal 
cross-section and frontal views. MVs are composed of three major structural elements: a dumbbell shaped core 
(C; red), two lateral bodies (LB; green), and the viral membrane (M; blue), encompassing these elements. Sub-
viral locations of the VACV proteins and components referred to in this report are illustrated. (c) Wide-field 
(left) vs structured illumination microscopy (right) images of a single VACV virion. Scale bars = 100 nm.

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dataset for F17 mapping, two models of F17 position emerged: in the first F17 was confined to a single circular 
structure centred on A4, in the second two oblong F17 structures horizontally flank the A4 core (Fig. 2b, III). 
Illustrating the power of VirusMapper for unbiased model generation, using multiple seed selection we obtained 
protein localization models that accurately represent the frontal and sagittal views of VACV particles as pheno-
typically defined by EM (Fig. 1a and ref. 9).
High-precision six-component model of VACV nanoarchitecture. 
These results suggested that it is 
possible to map a large number of viral proteins or components using the same viral feature as a reference. To this 
end we acquired multi-color SIM images of various viral components in combination with the core proteins A4 
or L4 as reference channels for mapping. To visualize membrane proteins, virions were subjected to immunofluo-
rescence directed against the membrane protein A17. The virus lipid membrane was visualized using the lipid dye 
CellMask. As in Fig. 1, to visualize the virus core and LBs, recombinant viruses expressing fluorescently tagged 
versions of the outer core protein A4, the inner core protein L4, or the LB constituent F17 were used. Finally, the 
DNA dye TO-PRO-3 was used for visualization of viral genomes.
Using VirusMapper we generated 2D, SR frontal and sagittal models of each virion component complete with 
their MSE (Fig. 3a and Supplementary Fig. 2). Measurements of each component in both frontal and sagittal 
views showed that the length and width of those imaged decreased as follows: viral membrane protein A17, the 
viral membrane, outer core protein A4, viral DNA, the inner core protein L4, and the LB protein F17 (Fig. 3b and 
Supplementary Table 1).
Next, to access the accuracy of this approach, we compared the measurements of our model VACV 
with those obtained by cryo-ET and atomic force microscopy (AFM)9,17. Consistent with the cryo-ET 
[360(l) ×  270(w) ×  250(h) nm] and AFM [300–360(l) ×  240–280(w) ×  240–290(h) nm] measurements of 
intact VACV, our model VACV has an average size of 356 (l) ×  288 (w) ×  264(h) nm as determined by CellMask 
labelling (Fig. 3b and Supplementary Table 1). The core length of our model (281 ±  3 nm) was also consistent 
Figure 2. Information extraction using VirusMapper: Segmentation, Seed Selection, and Model 
Generation. (a) VirusMapper workflow: Detection & Segmentation: a particle detection algorithm detects 
and realigns (Realignment) viral particles based on their shape and symmetry (VACV A4 pictured). Seed 
Selection: from the aligned particles a subset of representative viral images is selected based on user-defined 
characteristics (a priori knowledge of core shape). Model Generation: the aligned particles are matched against 
the seed (Matching) and normalized by cross-correlation similarity to obtain the model MSE and concomitantly 
to iterate the process until the smallest MSE is obtained. The resulting models can also serve as new seeds for 
further model improvement. (b) Multi-component model generation. The process illustrated in (A) can be 
repeated to generate multi-component models of single virions. For this, more than one seed selection criteria 
is applied to a single reference channel (VACV A4 frontal and sagittal pictured). Additional virion components 
(VACV F17 pictured) are aligned to the reference for the generation of virion orientation-based (frontal and 
sagittal) models of various viral components. Scale bars = 100 nm.

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with that previously reported for VACV by ET (275 ±  18 nm)18. When measurements were made of the VACV 
model generated using images of the viral membrane protein A17, we noted a ~60 nm increase in virion size 
(420 ×  337 ×  314 nm) (Fig. 3b and Supplementary Table 1). As A17 was visualized by immunofluorescence this 
increase suffers from the inherent error contribution of using primary and secondary antibodies in SR micros-
copy19. We also noted that although the viral genome and DNA binding protein L4 largely overlap, the DNA 
extended beyond the L4 signal towards the periphery of the core (Fig. 3b and Supplementary Table 1). This 
observation is in agreement with cryo-ET data showing the viral DNA in close association with the core wall9,18.
That differences in the size of virion components residing in the core substructure (A4, DNA and L4) were 
seen demonstrates that the models generated by VirusMapper are of high precision. Composite images of closely 
Figure 3. Mapping of VACV components in multiple SR modalities. (a) Frontal and sagittal models of six 
virion components generated using VirusMapper including membrane protein A17, the viral lipid bilayer (Cell 
Mask; CM), viral outer core protein A4, the viral DNA, the inner core protein L4, and the LB component F17. 
Scale bars = 100 nm. Representative individual particles (left; grey) and resulting models (right; color). (b) Length 
and width measurements of each virion model. Bars represent the range of the cross-validation distributions for 
each. (c) Models of vaccinia virus nanoarchitecture. Composite images of size descending frontal models and 
their precision (LD; length difference) as well as a composite sagittal model of all mapped virion components: 
A17 (green)/CM(red), CM(green)/A4(red), A4(green)/DNA(red), A4(green)/L4(red), DNA(green)/L4(red) and 
a composite of all sagittal models A17(cyan)/ CM(yellow)/ A4(red)/ DNA(blue)/ L4(green)/ F17(magenta). Scale 
bar = 100 nm. (d) STED models of the three major subviral structures. Membrane protein A17, core protein A4 
(both frontal orientation) and lateral body protein F17 (sagittal orientation). Representative individual particles 
(left; grey) and resulting models (right; color). Scale bars = 100 nm.

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apposed virion components, (A17 and the viral membrane, viral membrane and outer core protein A4, A4 and 
inner core protein L4, A4 and the viral DNA, L4 and the viral DNA) as well as the precision to which they could 
be mapped were generated (Fig. 3c). We found that the individual components could be mapped against each 
other within a distance of 19.7–69.6 nm with a precision of 3.1–4.6 nm as the standard deviation of the cross 
validation distribution.
VirusMapper can be applied to multiple super-resolution modalities. 
To investigate the applicabil-
ity of VirusMapper to super-resolution techniques other than SIM we imaged the VACV membrane, core and LBs 
using stimulated emission depletion (STED) microscopy. To visualize the viral membrane, the viral membrane 
protein A17 was stained with Alexa488 labelled antibodies. For visualization of the viral core and LBs, recombi-
nant viruses with EGFP-tagged versions of the core protein A4 and the LB protein F17 were used. To aid particle 
detection and alignment, A17 and F17 were simultaneously imaged with mCherry-tagged A4.
After STED imaging, VirusMapper was used to generate models of these three viral components (Fig. 3d). 
Although we were unable to match the resolution of the SIM models due to considerable fluorophore pho-
tobleaching at high depletion beam illumination, VirusMapper was successfully implemented to create accurate 
models of the three major viral components using images generated by STED microscopy. These results demon-
strate that VirusMapper can be applied to images acquired using various super-resolution methods.
VirusMapper can accurately resolve nanoscale changes in virus architecture. 
That extracellular 
viruses are highly stable structures dictates that their structure encodes inherent metastability to allow for effi-
cient genome delivery and subsequent progeny virion assembly within the same host cells3,20. These structural 
changes can occur as early as virus-cell binding, and in these cases are so minute that they have only been detected 
in a limited set of viruses using EM and ET2,18. Using whole cell cryo-ET, it was suggested that upon cell contact 
VACV cores undergo a shape change that correlates with genome de-condensation18. However, this was con-
cluded from a single VACV particle in the absence of molecular specific labelling. Therefore, we set out to eluci-
date the nature of these changes by applying VirusMapper to SIM images of cell-bound fluorescent VACV virions. 
For this mCherry-L4 VACV was bound to HeLa cells at 25 °C for 1 hour. At this temperature virions can attach to 
the cell surface but cannot be internalized21,22. After binding, cells were fixed and imaged by SIM. To assure all cell 
bound virions were visualized Z-stacks were collected through the height of the cells and converted to maximum 
intensity projections (Fig. 4a). The images were analysed by VirusMapper and models of L4 protein generated by 
analysis of approximately one thousand virions (Fig. 4b). The resulting models indicated that L4 within virions 
bound to the cell surface had condensed toward the center of the virion core. Measurement indicated that the 
position of L4 within virions had shifted away from the core wall by 20 nm in width and 13 nm in length relative 
to its position in isolated virions (Fig. 4c). As a viral DNA binding protein, the observed contraction of L4 is 
consistent with the release of the viral DNA from the core wall upon cell binding. These results illustrate that 
VirusMapper can be used to investigate minute nanoscale changes in virus metastability in the context of host 
cells at high-throughput and with great precision.
Discussion
VirusMapper enables the mapping of virus protein architecture by harnessing the power of SR microscopy and SPA. 
We were able to generate high precision multi-component 2D models of viral protein and substructure localization, 
and investigate nanoscale changes in viral protein architecture upon cell binding, each of which would have been 
impossible using conventional light microscopy, EM or ET. EM and ET have been valuable tools to determine the 
overall structural organization of viruses, including VACV9,11, and SR has already offered a look into the structural 
organization of some viruses4. When coupled with correlative EM technologies and applied to complex dynamic 
Figure 4. Detection of nanoscale changes in virion architecture using VirusMapper. (a) Purified VACV 
mCherry-L4 virions were bound to cells for 60 minutes at 25 °C and samples were fixed prior to staining with 
Hoechst to visualize nuclei. Samples were imaged by SIM and Z-stacks collected over the full cell height. The 
maximum intensity projection of a representative cell with bound virions (arrow heads and inset) is shown. 
Scale bar 5 μ m, inset scale bar 1 μ m (b) VirusMapper generated models of L4 protein in isolated and cell-bound 
viruses. Scale bar 100 nm. (c) Length and width measurements of L4 virion models in (b). Note different scales 
for length and width measurements.

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intracellular processes, such as virus uncoating or assembly, VirusMapper has the potential to facilitate understand-
ing of the molecular details of virus structural metastability with high precision and accuracy.
There are many software packages available for single particle analysis of data generated using cryo-EM23. 
However these require parameters not easily translated to light microscopy and are highly specialised for pro-
ducing 3D reconstructions of protein structure. They are therefore not readily applicable to fluorescence imag-
ing. The same averaging principles have been successfully applied to fluorescence1–4, but are not available as an 
easy-to-use, freely available package for the popular ImageJ and Fiji image analysis software.
By providing a fast, user-friendly ImageJ and Fiji23 based analytical framework for the naïve averaging and 
model extraction of SR images, we equip researchers with the possibility to map an unlimited number of proteins 
and components within viruses and other nanoscale macromolecular complexes at high precision on robust 2D 
and 3D models. Thus VirusMapper provides the opportunity to discover novel virus structures and uncover new 
structure-function relationships within virion protein architecture, which in turn may facilitate the development 
of effective antiviral agents.
Methods
Reagents. Cell Lines. BSC-40 cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM) sup-
plemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM Glutamax, 100 units/mL penicillin and 
100 μg/mL streptomycin, 100 μM nonessential amino acids and 1 mM sodium pyruvate.
Viruses. Recombinant VACV were based on VACV strain Western Reserve (WR). WR mCherry-A4 and WR 
GFP-A4 were previously described as WR mCherry-A5 and WR GFP-A5 24. WR L4-mCherry EGFP-F17 was 
previously described as WR EGFP-F17 VP8-mCherry16. MVs were produced in BSC-40 cells, purified from cyto-
plasmic lysates and banded on a sucrose gradient, as previously described25.
Antibodies and Dyes. Rabbit polyclonal antibody anti-A17 (p21 N-terminal)15 was a kind gift of Jacomine 
Krijnse-Locker (Institut Pasteur, Paris, France). AlexaFluor 488 and AlexaFluor 594 coupled goat anti-rabbit 
secondary antibodies, the membrane stain CellMask™ Deep Red and Green Plasma membrane stains and the 
nucleic acid stain TO-PRO-3 Iodide were purchased from Life Technologies.
Super-Resolution Microscopy Sample Preparation. 
High performance coverslips (18 ×  18 mm 
1.5 H, Zeiss) were washed as previously described26. Purified virus was diluted in 10 mM Tris pH 9, bound to 
the ultra-clean coverslips for 30 min and fixed with 4% Formaldehyde. To visualize the viral DNA, virus bound 
to the coverslip was incubated with 1 μ M TO-PRO-3 Iodide for 1 hour. Samples were washed 3 times with PBS 
prior to mounting for imaging. To visualize the viral membrane, virus was blocked after fixation using 5% FBS 
in Phosphate Buffered Saline (PBS) for 30 min, incubated in 1% BSA in PBS with primary anti-A17 antibody, 
followed by (488 or 594) goat anti-rabbit fluorescent secondary antibodies for 1 hour each. Three PBS washes of 
10 min each were performed between each staining step. For CellMask staining, 5 μl of purified virus was incu-
bated with 1 μl CellMask for 20 min and washed twice with 500 μl of PBS. The coverslips corresponding to the 
different stainings were mounted in Vecta Shield (Vector Labs).
Super-resolution Structured Illumination Microscopy (SR-SIM). 
SR-SIM imaging was performed 
using Plan-Apochromat 63 ×  /1.4 oil DIC M27 objective, in an Elyra PS.1 microscope (Zeiss). Images were 
acquired using 5 phase shifts and 3 grid rotations, with the 647 nm (32 μ m grating period), the 561 nm (32 μ 
m grating period), the 488 nm (32 μ m grating period) and 405 nm (32 μ m grating period) lasers, and filter set 3 
(1850–553, Zeiss). 2D images were acquired using a sCMOS camera and processed using the ZEN software (2012, 
version 8.1.6.484, Zeiss). For channel alignment, a multicolored bead slide was imaged using the same image 
acquisition settings and used for the alignment of the different channels.
Super-resolution Stimulated Emission Depletion (STED) microscopy. 
Time-gated STED imaging 
was carried out using a 100 x HC PL APO CS2 oil immersion objective (NA 1.4) on a Leica TCS SP8 STED 
3x microscope running LAS X (Version 2.01.14392) acquisition software. GFP and Alexa Fluor 488 fluores-
cence were excited using the 488 nm line (8–10%) and mCherry fluorescence using the 561 nm output (10%) 
from a white light laser (WLL, operating at 70% of its nominal power). Fluorescence depletion, and therefore 
super-resolution, was accomplished using a 592 nm STED laser (1.5 W nominal power, 90% for GFP and 100% for 
Alexa Fluor 488). All 2048 ×  2048 pixel single plane images were a acquired at a scan speed of 400 Hz in bidirec-
tional scan mode. The pixel size of 14.20 nm2 was optimized for STED imaging. The fluorescence signal detected 
by a Hybrid Detector (HyD, Standard mode) after passing through an Acousto-Optical Beam Splitter (AOBS, 
detection range 495–585 nm for GFP alone, and 495–525 nm for Alexa Fluor 488 or GFP and 600–700 nm for 
mCherry when doing two-colour imaging). Time-gated detection was turned on to further improve the resolu-
tion in the STED images (0.3–6.0 ns for GFP and Alexa Fluor 488) or remove coverslip reflections in the confocal 
channel (0.5–8.0 ns for mCherry). The detector gain was adjusted so that no saturation occurred in the images.
SIM Vaccinia Virus Analysis with VirusMapper. 
For each virion component modelled, 10,000–15,000 
viral particles were extracted. For each model, seeds were created by averaging 3 to 6 individual, user selected, 
particles. For model generation, a similarity cut-off against the generated seeds was set between 95% and 99%, 
yielding a selection of ~1000 particles for model generation. In our investigations of vaccinia we found it neces-
sary to distinguish between two orientations, frontal and sagittal, corresponding to the largest and second-largest 
face of the virus perpendicular to the optical axis respectively (Fig. 2b). Viruses were imaged on the coverslip in 
a random range of orientations, although we assumed cases where the virus was resting on its smallest face, in a 

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transverse orientation, to be few. We distinguished between orientations in two ways. Firstly and most robustly, 
when imaging the WR L4-mCherry EGFP-F17 viruses the clear separation of the lateral bodies (F17 channel) 
reveals a sagittal orientation. We assumed those where only one lateral body was evident to be broadly in the fron-
tal orientation. Secondly, in the case of the WR mCherry-A4 the core (A4 channel) was noticeably larger in the 
frontal orientation, consistent with the EM observations. Furthermore in some cases two maxima, arising due to 
the biconcave “peanut” shape of the core, were visible. This informed our choice of seed leading to separate frontal 
and sagittal models. The A4 and CellMask models involved this latter case to distinguish orientations and the 
remaining four models involved the former. We constructed the models as described in Supplementary Note 1. 
Seeds were chosen as the clearest representation of the frontal or sagittal orientation and the minimum similarity 
threshold was chosen to try to exclude particles of the wrong orientation.
STED Vaccinia Virus Analysis with VirusMapper. 
For the STED models, around 5000 virus particles 
were extracted from series of images. Seeds and models were generated in the same way as for the SIM models, 
although with a similarity cutoff of between 92 and 95%, yielding around 500 particles per model.
Vaccinia Virus Model Measurements and Cross-validation Analysis. 
To quantify the size of the core 
models (A4, L4 and DNA) we fitted a single Gaussian to the pixel values along the vertical and horizontal axes 
through the centre of the image and took the full width at half maximum (FWHM) as the length and width 
respectively. Similarly, we measured an estimate of the length and width of the lateral body model (F17). In 
the frontal and sagittal orientations the frontal length and width and the sagittal length are broadly the same. 
Viruses on the coverslip diverging from a frontal or sagittal orientation incorporated into the analysis contribute 
to the variation of these values. To quantify the size of the membrane models (A17 and CellMask) we fitted two 
Gaussians to the pixel values along the vertical and horizontal axes. Our estimates for the length and width are the 
combined width of these two Gaussians. That is, the distance between their centres plus half the FWHM of each. 
Borrowing from Szymborska et al.6 in order to estimate the precision of our model measurements we performed 
cross-validation analysis on the data. Each model was the average of at least 1000 particles. We then split these 
particles in random, non-overlapping subsets of 50 particles, averaged each of these subsets and made measure-
ments for these averages, giving a range of values clustered around the model measurement. The precision was 
then calculated as the standard deviation of these measurements (Fig. 3b, and Supplementary Table 1). In Fig. 3 
the precision for the difference measurements was obtained by adding in quadrature.
Cell-based SR-SIM Sample Preparation. 
BSC40 cells were grown on coverslips (18 ×  18 mm 1.5 H 
Zeiss). Purified virus was diluted in DMEM at a MOI of ~2, bound to cells for 60 minutes at room temperature 
and then samples were fixed in 4% formaldehyde for 20 minutes and washed 3 times with PBS. Cell membranes 
and nuclei were visualized with CellMask Green (ThermoFisher) and Hoechst (Life Technologies), respectively. 
Samples were imaged as described above for purified viruses. Z-stacks were taken over the full height of the cells 
at an interval of 100 nm. Maximum intensity projections were generated and were analysed with VirusMapper 
as described.
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Acknowledgements
We thank Dr. Jacomine Krijnse-Locker for reagents. This work was funded by grants from the Biotechnology 
and Biological Sciences Research Council (BB/M022374/1) (R.H.), core funding to the MRC Laboratory for 
Molecular Cell Biology, University College London (J.M.), the European Research Council (649101—UbiProPox) 
(J.M.)., and the Medical Research Council (MR/K015826/1) (R.H. and J.M.). R.G. is funded by the Engineering 
and Physical Sciences Research Council (EP/M506448/1). C.B. is funded by the Medical Research Council LMCB 
PhD program. J.S. is funded by the Biotechnology and Biological Sciences Research Council LIDo PhD program 
(BB/J014567/1). Software can be downloaded at https://bitbucket.org/rhenriqueslab/nanoj-virusmapper/.
Author Contributions
R.D.M.G., C.B., P.M.P., R.H. and J.M. designed the study. R.D.M.G. and R.H. wrote the algorithm. R.D.M.G., 
C.B., P.M.P., J.S., R.H. and J.M. planned experiments. R.D.M.G., C.B., P.M.P., K.M.S., J.S. and J.M. carried out 
experiments. All authors contributed to writing the manuscript.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Gray, R. D. M. et al. VirusMapper: open-source nanoscale mapping of viral architecture 
through super-resolution microscopy. Sci. Rep. 6, 29132; doi: 10.1038/srep29132 (2016).
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