Since the invention of the microscope in the 16^th century and the subsequent discovery of a world concealed from our bare eyes, advances in experimental science and technology have excelled our understanding of cell biology. Modern imaging techniques were central to unravelling the intricacies of the cellular landscape at a molecular level. X-ray crystallography, NMR spectroscopy and cryo-electron microscopy achieve atomic- or near-atomic resolution of individual proteins and cell architecture, while live-cell imaging enables observation of cellular structures, processes and behaviours in real-time. Further, the use of enhanced fluorescent tags and biosensors has shed considerable light on protein kinetics, interactions and diffusion (Wollman et al., 2015; Goodsell et al., 2020).

Three-dimensional (3D) visualisation of scientific concepts and experimental data is nowadays a popular tool to provide insight where traditional, two-dimensional graphical illustrations or descriptive text cannot (Goodsell and Jenkinson, 2018). The educational benefits are numerous, from clarifying complex or abstract concepts to testing hypotheses and generating new ideas (Daly et al., 2016; Jenkinson and McGill, 2012; McClean et al., 2005; Kadir et al., 2020).

However, no single experimental modality is sufficient to elucidate the structure and dynamics of macromolecular assemblies and cellular processes. Therefore, integrative modelling of data from multiple, complementary techniques and scientific disciplines is crucial (Ornes, 2016). Scientific journals and bioinformatics databases contain a wealth of such data, but the extraction, interpretation and consideration of the relevant data is challenging (Conesa and Beck, 2019). Moreover, many biomedical visualisations do not cite journals or databases, meaning that users do not know if a given visualisation has been informed by empirical data, or if it owes more to artistic license (Goodsell and Johnson, 2007; Jantzen et al., 2015).

Biomedical animators often use 3D computer animation and modelling software popular in the games and entertainment industry, such as Autodesk Maya (https://autodesk.com/maya), SideFX Houdini (https://www.sidefx.com/products/houdini/) and Pixologic Zbrush (http://pixologic.com/features/about-zbrush.php). However, the true complexity of the cellular environments is often deliberately diminished to clarify or emphasise features and mechanisms of interest. It may also be reduced for other reasons, such as technical limitations of computer graphics or time constraints (Goodsell and Johnson, 2007).

The last couple of decades have seen an increase in visualisation software tools and accurately scaled, static reconstructions at the molecular level. Notable examples are the HIV-1 virus and Mycoplasma mycoides (created with the packing algorithm CellPACK), and a snapshot of a synaptic bouton obtained through integrating a plethora of imaging techniques, quantitative immunoblotting and mass spectrometry (Johnson et al., 2015; Johnson et al., 2014; Wilhelm et al., 2014). Previously, simulations of Brownian or molecular dynamics have been incorporated into 3D atomic resolution models of bacterial cytoplasmic subsections to examine the effect of the interactions, stability and diffusion of proteins in crowded cellular environments (Feig et al., 2015; Yu et al., 2016; McGuffee and Elcock, 2010).

Furthermore, mathematical and computational modelling platforms such as V-Cell, M-cell, and E-cell are designed to be used by experimental biologists and theoretical biophysicists (Moraru et al., 2008; Stiles and Bartol, 2001; Stiles et al., 1996; Tomita et al., 1999). These platforms can run simulations of cell biological phenomena based on a combination of multiple ‘omics’ technologies and imaging data, thereby enabling scientists to analyse 3D representations of their raw data and test hypotheses with varying degrees of molecular and spatial resolution.

However, many of the existing techniques described above do not fully represent cellular environments, mostly due to sheer complexity but also because of lack of funding or knowledge, or computational limitations. Our previous project (Journey to the Centre of the Cell) already showed significant improvement in the students’ comprehension of cellular structures and processes (Johnston et al., 2018). It provided an immersive, virtual reality educational experience of an entire 3D cell based on serial, block-face scanning electron microscope imaging data. Although the project successfully depicted cellular features from real microscopy data, portrayal of the cell surface environment had to be oversimplified due to various constraints, including working at higher virtual reality frame rates (over 90 fps) and a smaller development team.

Nanoscape is a collaborative follow-up project between computer artists, experts in computer graphics and cell biologists to create an interactive real-time open-world experience that enables a user to navigate a cell terrain within a tumour microenvironment. This first-of-its-kind application distils and integrates a vast archive of scientific data into an interactive immersive experience using a comprehensive library of existing visualisation tools, while employing distinct principles of art and design. Nanoscape is primarily an educational tool for tertiary-level science students, and it is available at https://store.steampowered.com/app/1654050/Nanoscape.

Here, we present some of the challenges and limitations experienced during the data collection process and the conceptualisation of 3D assets, and examine the practicability of visually replicating experimental data. We discuss the implications of gaps in scientific knowledge, modification or simplification of data, and the use of artistic license for visual clarity. Our work raises important questions about whether molecular visualisations can support outcomes beyond the educational field and could help experimentalists to better understand their data.

As part of the pre-production stage, information on the major surface components, extracellular features and processes commonly found in breast cancer scenarios was collated through a comprehensive review of the literature along with analysis of experimental data obtained from scientific collaborators (Figure 1). This cell type was chosen as the focus of the visualisation due to the abundance of protein and structural data available from these sources.

Figure 1

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Cell surface proteins

Cell surface proteins, collectively known as the surfaceome, exhibit a wide range of roles, from playing a vital role in communication between the cell and its environment to signal transduction and transport of ions and other small molecules (Bausch-Fluck et al., 2018). The Cell Surface Protein Atlas (wlab.ethz.ch/cspa) and the in silico human surfaceome (wlab.ethz.ch/surfaceome), which together have classified 2886 entries, were used to first identify different types of surface proteins, such as receptors, soluble and membrane transport proteins (Bausch-Fluck et al., 2015; Bausch-Fluck et al., 2018). Subsequently, over 30 prevalent surface proteins associated with breast cancer that were available from the RSCB Protein Data Bank (PDB) were selected and organised in a cast of characters (see Appendix 1 and Figure 2; Berman et al., 2000). Where possible, any available information on their motion (molecular dynamics, conformational changes and protein interactions), and population densities were interpreted from various published sources.

Figure 2

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The structural and dynamic information gathered about the ErbB family of proteins (which consists of 4 receptor tyrosine kinases: EGFR, Her2, Her3, and Her4) and the associated literature references used to create mechanism of action animations for each family member are summarised in Figure 3 and Appendix 2. Molecular Maya (mMaya) modelling and rigging kits (https://clarafi.com/tools/mmaya/), which qualitatively replicate molecular dynamics, were used to simulate ligand binding events and transitions between conformational states (see Materials and methods). These rudimentary mechanism of action animations provided an interpretation of protein movement based on experimental data available and were subsequently used to inform the artistic design team on how best to approach rigging a refined, stylised 3D protein mesh using traditional rigging methods (Figure 3B).

Figure 3

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Conformational flexibility plays a crucial role in enabling protein-ligand interactions, multi-specificity and allosteric responses. Unfortunately, most proteins have no reliable, or only partial experimentally determined 3D structures, available. Such limitations may lead to a presentation of a single, ‘native’ structure in visualisations, instead of multiple flexible conformational variations, further perpetuating misconceptions about protein structure, folding, stability and effects of mutations (Robic, 2010). Where possible, Nanoscape used several conformational states of proteins sourced from the PDB as well as conformational changes simulated with mMaya (see Materials and methods). The choice of protein mesh detail and representation will also affect the viewer. A low poly mesh will significantly reduce the computational burden in rendering but may compromise important scientific information, such as the specificity of a ligand binding pocket that may be essential for conveying the mechanism of action. Nanoscape incorporates a level-of-detail feature that reduces the polygon density of proteins as a function of distance to the user, thereby decreasing the computational burden of distant proteins without affecting perceived detail. It also allows more proteins to be displayed on screen simultaneously.

Protein dynamics can range from localised movement in specific residues to large rearrangements in domains and multiple subunits. Representing these broad spatio-temporal scales is a major challenge for biomedical animators. Protein bond vibrations and domain motions can range from femtoseconds to milliseconds respectively, and in turn, many cellular processes occur in the order of seconds to minutes (McGill, 2008; Miao et al., 2019). Multi-scale representations in landscape animations of cells often have constrained computer graphics performances, sacrificing atomic resolution and motion.

Cellular environments are heterogeneous, highly dynamic and densely packed. Up to 20% to 30% of intracellular environments is occupied by macromolecular components (Goodsell et al., 2020). Molecular crowding is known to influence the association and diffusion of proteins, as well as the rates of enzyme-catalysed reactions (Mourão et al., 2014). Yet many visualisations eschew depicting stochastic motion and extreme crowding due to fear of losing focus on the visual narrative or cognitive overload (Goodsell and Johnson, 2007; Jenkinson and McGill, 2012). Furthermore, the computational expense involved with animating and rendering large numbers of meshes is a significant hurdle. However, underrepresentation or oversimplification has been shown to worsen deep rooted misconceptions, particularly amongst students (Garvin-Doxas and Klymkowsky, 2008; Gauthier et al., 2019; Jenkinson et al., 2016; Zhou et al., 2008). Indeed, our inherent ability to picture such numbers in real life is a challenge, and therefore it is useful to perform ‘sanity checks’ (Zoppè, 2017).

We assessed the feasibility of replicating receptor densities based on empirical data, using the 3D computer graphics application Blender (https://www.blender.org/) and the plugin autoPACK (Johnson et al., 2015). The autoPACK algorithm can fill compartmental volumes or surfaces with user defined meshes or protein meshes retrieved from the PDB and has been previously used to generate models of HIV, blood plasma and synaptic vesicles (Johnson et al., 2015; Takamori et al., 2006).

Receptor density values (number per μm²) of six well-known surface biomarkers on MDA-MB-231 cells from flow cytometry data were modelled on 1 μm² area test patches (Cahall et al., 2015). The packing simulations revealed 12,650 proteins of variable sizes were easily accommodated with moderate molecular crowding (Figure 4). The diversity of surface proteins varies significantly between different cell types, and whilst a fairly equal distribution was modelled for this scenario, proteins are often clustered in functional units or spread unevenly. In addition, the limitations of experimental techniques ought to be scrutinised, for instance, variations in the specificity of antibodies measured with flow cytometry could potentially lead to under- or over exaggeration of numbers. Often, the receptor density data measured is only a snapshot in time, while the receptor population on a cell is highly dynamic and stochastic. Nevertheless, further exploration of experimentally derived population densities using packing algorithms such as autoPACK will improve our understanding of surface protein distribution and organisation.

Figure 4

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A negative consequence of choreographed molecular visualisations is that they can often allude to ‘directed intent’, the most common example being a ligand moving undisturbed towards a receptor, which subsequently leads to immediate binding. A more authentic depiction might be a random walk (constrained by kinetics and thermodynamics) through a packed environment, where the ligand undergoes many non-specific interactions before binding successfully (Jenkinson and McGill, 2012; Robic, 2010). Biomedical artists may be disinclined to animate unsuccessful binding events to save on animation and render time, but this will only deepen superficial understanding or promote misconceptions. Whether visual clutter in crowded molecular environments has a negative impact on audiences is a point of contention. Careful use of graphic devices such as titles or arrows, narration and colour can improve complex visualisations and understanding (Jenkinson et al., 2016; Jenkinson and McGill, 2012; McClean et al., 2005).

Cellular processes

Cellular processes fall within the biological mesoscale, which is an intermediate scale range between nanometre-sized molecular structures and micrometre-sized cellular architecture (Goodsell et al., 2018; Laughlin et al., 2000). An integrative modelling approach was adopted whereby information from multiple sources was combined for depiction of mesoscopic processes on the cell surface. Surface processes of interest were categorised as either protrusion (filopodia), invaginations (caveolae, clathrin-coated pits, or macropinocytosis) or extracellular vesicles (exosomes; see Table 1). Scanning electron microscopy and transmission electron microscopy data were used to calculate the approximate dimensions of typical breast cancer cells, along with sizes and densities of protrusions and invaginations (Figure 5). These measurements were in accordance with published data. Similarly, information on temporal dynamics was taken from research literature (Table 1). Since these data fell within a broad temporal range varying from seconds to minutes, cellular features were modelled and animated using 3D software Zbrush and Maya (Figure 5 and Video 1).

Figure 5

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Table 1

	Feature	Examples	Dimensions	Density	Temporal dynamics
Structures	Membrane bound receptors1	EGFR, Integrins, VEGFR	~5–20 nm length; ~1–5 nm diameter	Protein specific ~10–104 per µm2	Protein specific D ~ 10−3–1 µm2/s [see note 10]; Transitions between protein states ~ 1–100µs
	Soluble proteins1	EGF, TNF, MMPs
	Membrane transport proteins2	GLUT4, K+ channel, Na+/K+ pump	~4–10 nm length	~104 per µm2	~100–107/s transport rate
	Extracellular matrix3	Collagens, Fibronectin, Hyaluronic acid	Variable: from large fibres to smaller glycoproteins	Variable	Very low mobility relative to proteins
	Plasma membrane4	Phospholipids	~2 nm length;~0.25–0.5 nm2 cross-sectional area	~5×106 per µm2	†D ~ 1 µm2/s
Processes	Protrusions	Filopodia5	~1–5 µm length; ~150–200 nm diameter	~0.3 per µm2	~25–50 nm/s protrusion rate
	Endocytosis	Caveolae6	~65 nm mean diameter; ~0.0067 µm2 area	~10 per µm2	~30 s to minutes
		Clathrin mediated endocytosis7	~110 nm mean diameter; ~0.0190 µm2 area	~0.8 per µm2	~30–60 s
		Macropinocytosis8	~0.2–5 µm diameter	?	~120 s
	Extracellular vesicles	Exosomes9	~40–150 nm diameter	?	?

1. * Key features of cellular structures and processes in Nanoscape, with examples detailing properties such as dimensions, densities, temporal dynamics. See Figure 1 for 3D models.

   Notes: 1 Membrane bound receptors and soluble proteins. Milo et al., 2010. 2 Membrane transport proteins. Milo et al., 2010. Chapter IV in Milo and Phillips, 2015. Page 9 (top paragraph) in Itzhak et al., 2016. Table 8.3 in Gennis, 1989. 3 Extracellular matrix. Frantz et al., 2010. Insua-Rodríguez and Oskarsson, 2016; Früh et al., 2015; Mouw et al., 2014; Pankov and Yamada, 2002. 4 Plasma membrane lipids. Milo et al., 2010. Chapter II in Milo and Phillips, 2015. Chapter 10 in Alberts et al., 2002. Table 1 in Rawicz et al., 2000. Page 2644 (right column, 2nd paragraph) in Brügger et al., 2006. 5 Filopodia density and dimensions. Measured from scanning electron micrographs, see Figure 5 in Mallavarapu and Mitchison, 1999. 6 Caveolae density, dimensions and temporal dynamics. Parton, 1994; Parton et al., 2020a; Parton et al., 2020b; Pelkmans and Zerial, 2005; Boucrot et al., 2011; Richter et al., 2008. 7 Clathrin mediated endocytosis density, dimensions and temporal dynamics. Cocucci et al., 2012; Doherty and McMahon, 2009; Edeling et al., 2006; Kirchhausen, 2009; McMahon and Boucrot, 2011; Merrifield et al., 2002; Parton, 1994; Saffarian and Kirchhausen, 2008; Taylor et al., 2011. 8 Macropinocytosis dimensions. Condon et al., 2018; Lim and Gleeson, 2011. 9 Exosome dimensions. Skotland et al., 2017. 10 Diffusion coefficient. D is microscopically determined by the velocity of the molecule and the mean time between collisions.

Video 1

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Extracellular matrix

The extracellular matrix (ECM) surrounding cells is an extensive and complex network of structural fibres, adhesion proteins and glycosaminoglycans. It works as a scaffold for the cellular environment and can also influence behaviours, processes and communication of a cell (Frantz et al., 2010; Insua-Rodríguez and Oskarsson, 2016; Mouw et al., 2014). The ECM consists of two biochemically and morphologically distinct forms: the basement membrane, which is a thin layer that forms between epithelial and stromal cells, and the adjacent interstitial matrix, which is a more loosely organised network surrounding cells (Mouw et al., 2014).

Despite its vital role, the ECM is often overlooked in many cellular and molecular visualisations due to several reasons. Firstly, high resolution imaging of the ECM in its native state is inherently difficult in opaque tissue. Whilst optical tissue clearing and decellularisation methods are routinely used to enhance the visibility in stained tissues and organs, they are often harsh treatments that may modify the physical and chemical properties of the ECM (Acuna et al., 2018). In addition, many in vitro models consisting of a limited mix of collagens and matrix proteins, such as cells suspended in 3D gels, may not always be physiologically relevant.

The components within the matrix are often very large macromolecular complexes, and although complete protein sequences are available on Uniprot, many PDB structures are only of truncated or incomplete proteins (UniProt Consortium, 2018). Consequently, it is an arduous task to visualise these large ECM structures in their entirety, and some artistic license may be needed.

To visualise the ECM within a 3D breast tumour microenvironment, we collated information from the literature, including images, and sought advice from experts in the field during the conceptualisation stage (Figure 6A; Table 1). Although there are a multitude of ECM components that make up the tumour microenvironment, for simplicity it was decided the focus would be only on four key ECM players: collagens I and IV, hyaluronic acid and fibronectin (Mouw et al., 2014). In breast cancer, there is usually increased deposition of collagen and fibronectin, and a significant disruption of collagen IV basement membrane networks (Frantz et al., 2010).

Figure 6

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A stylised artistic approach was adopted to build 3D meshes of the ECM using the modelling program Zbrush. This significantly reduced the polycount of large complex atomic macromolecules, which would otherwise be computationally expensive to render (Figure 6B–D; Materials and methods). Following advice from an ECM expert (S. Kadir personal communication with Dr Thomas Cox, Garvan Institute, July 2019), an artistic impression of a tumour microenvironment niche was built, highlighting interactions between integrin molecules on the cell surface and meshes of the basement membrane and interstitial matrix (Figure 6E).

Due to significant gaps in knowledge, many visualisation challenges were identified early on during the conceptualisation stage. Exactly how ECM proteins interact with one another to eventually form higher order structures such as fibrils, fibres and ultimately matrices, is very unclear. ECM remodelling is a highly dynamic ongoing process, which involves both proteolytic breakdown of existing matrix components by matrix metalloproteases (MMPs) and deposition of new components by cells. This intrinsic activity has significant implications on tissue development, cell migration and pathologies including cancer (Frantz et al., 2010; Insua-Rodríguez and Oskarsson, 2016; Mouw et al., 2014). However, there is not enough discernible data in the literature to reliably inform anyone trying to represent these processes, and as such, much of this detail was omitted from the ECM represented in Nanoscape.

Many molecular visualisations fail to show connections between the cell surface and the ECM (via cell adhesion molecules binding to ECM components) or veer away from even attempting to represent ECM density in vivo. This may be a combination of incomplete information about precise binding interactions, and a reluctance to complicate a scene for fear of cognitive overload. However, its frequent omission or aggressive simplification will only exacerbate a naive view of the ECM in vivo, whereas there is evidence that more complex molecular representations can in fact improve understanding (Jenkinson et al., 2016; Jenkinson and McGill, 2012).

Tumour microenvironment components

In addition to malignant cells and the ECM, a breast tumour microenvironment is made up of a complex mix of cells (including immune cells, fibroblasts, pericytes and adipocytes), blood vessels, lymphatics and various signalling molecules, and many studies show that cancer cells significantly impact their environment; it has also been shown that interactions with non-transformed cells and the tumour vasculature promote the progression of cancer (Balkwill et al., 2012; Quail and Joyce, 2013; Insua-Rodríguez and Oskarsson, 2016). To build a more comprehensive tumour microenvironment, additional breast cancer cells, cancer-associated fibroblasts and a leaky blood vessel with an animated blood flow were incorporated into the Nanoscape scene (Figure 7; Video 2; Materials and methods).

Figure 7

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Video 2

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Nanoscape is distinct from other published molecular and cellular visualisations, being a data-informed artistic innovation that permits user exploration and reflection of a tumour microenvironment. The Nanoscape scene was compiled in the real-time graphics engine Unity3D (https://unity.com/) and can be currently viewed in engine on a desktop gaming PC (with minimum of 1080 GTX GPU). Here, the user is essentially shrunk down to an equivalent height of 40 nm and is able to walk on the surface of a single cancer cell within a discrete play area surrounded by the ECM, neighbouring cancer cells, cancer-associated fibroblasts and a leaky blood vessel to observe surface proteins and cellular processes moving in real-time (Figure 8).

Figure 8

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Since the user experience was fundamental to the project, it became clear that true depiction of extreme molecular crowding and the ECM had to be compromised. Extraneous complexity such as the density of soluble molecules present in the extracellular space was significantly diminished, water and ions implicit, lipid meshes were substituted with a texture to mimic their form and dynamics, and the ECM reduced to only collagen I fibres, to enable greater visibility of the landscape.

Similarly, it was computationally demanding and aesthetically unfavourable to replicate the broad temporal ranges of both molecular and cellular processes. Whilst the dynamics of the major cellular processes could be animated accurately, atomic resolution was sacrificed, and protein conformational movement was appreciably slowed down to enable viewers to observe protein-protein interactions more clearly. To partially compensate for these diminutions, some artistic license was applied to evoke the ‘feel’ of a densely packed cancerous milieu through use of colour, lighting, and sound, even if nanoscale cellular entities are below the wavelength of light and devoid of noise.

Nanoscape has significantly surpassed the level of cell surface detail and complexity found in Journey to the Centre of the Cell. And although it is not fully immersive or interactive as the virtual reality experience of Journey to the Centre of the Cell, it has been carefully choreographed to engage the viewer to appreciate the heterogeneity of a tumour microenvironment, without overwhelming them with minutiae and promote understanding of some cell biology fundamentals. Future work involves the development of user-defined control features, which empower the viewer to focus in or out of regions to switch between observing atomic detail to large cellular processes, and adjustable dials for regulating molecular density and temporal dynamics, to experience a more authentic cellular environment.

Nanoscape is an innovative collaboration that has produced a multi-scale, explorable 3D environment of a cell. Our work sheds light on some of the technical and creative processes, decisions and sacrifices made in depicting cell surface entities and dynamics as close to experimental data as possible, whilst balancing concerns for the user experience and visual aesthetics. Although initially its main purpose was to be a unique educational and outreach tool to communicate some of the complexities of a tumour microenvironment, the final visualisation experience may also help experimentalists to reflect upon their own data.

Integrative modelling and visualisation of biomolecular systems and multi-scale cell models are becoming increasingly sophisticated, and immersive, virtual field trips to a cell environment such as Nanoscape may provide insights into function and behaviour of a cell. As computer hardware and software continues to evolve to cope with processing enormous amounts of data, improved visual or interactive 3D representations may one day lead the way for scientists to perform in silico experiments and potentially help with the development of new drugs.

Ideally, such a model would fully reflect the spatio-temporal complexities and heterogeneity of the entire cell and its environment and would be capable of continuous iterations and be falsifiable. To accomplish such an ambitious feat, researchers and developers from multiple scientific, computer graphics and design fields must work together.

All data generated or analysed during this study are included in the manuscript and supporting files. Protein structures were sourced from the publicly accessible Protein Data Bank.

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Thank you for submitting your article "Meta-Research: Nanoscape, a data-driven 3D real-time interactive virtual cell environment" to eLife for consideration as a Feature Article. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by two members of the eLife Features Team (Helga Groll and Peter Rodgers). The following individuals involved in review of your submission have agreed to reveal their identity: Gael McGill (Reviewer #1); Jodie Jenkinson (Reviewer #2).

The reviewers were mostly positive about the article, but they raised a number of concerns, so we are inviting you to submit a revised version of the manuscript that addresses these comments.

Reviewer #1:

Nanoscape, a data-driven 3D real-time interactive virtual cell environment by Kadir et al. describes the creation of an advanced molecular and cellular landscape that can be explored interactively by users. The authors describe the numerous technical and design considerations and challenges inherent to such an endeavor and provide detailed information on both the software and workflow processes they employed to meet these challenges.

A clear strength of the paper is not only the multidisciplinary approach that was used to tackle such a complex visualization, but also the extensive technical expertise brought to bear on this task. Whereas many biomedical animators or teams of animators often rely on a relatively limited (yet advanced) software solution to create their work, McGhee et al's team explored the use of many advanced solutions and deployed them where most relevant. This team combined software ranging from 'Hollywood-style' animation packages (Maya, Houdini, Blender), specialized plugins that allow not only structural and other scientific data to be brought in to these 3D packages but also to create guided/coarse-grained conformational morphs and procedural environment creation (BioBlender, Molecular Maya, autoPack), molecular graphics packages commonly used in the scientific community (Chimera), molecular dynamics simulations (CHARMM), and finally game engine technology (Unity). This is an impressive array of software solutions to deploy and the authors do so with skill.

Despite the technical prowess and impressive visual result, one unfortunate weakness of the paper as currently written is the lack of a clear definition of the 'target user' and a more explicit statement of the intended learning objectives (or intended impact on the user). While the authors do a nice job of addressing the myriad limitations intrinsic to attempting an 'authentic' visualization of molecular and cellular worlds, they do not spend much time describing the design parameters that actually drove their decision-making process. This is touched upon in the 'Nanoscape User Experience' section, but in a rather fleeting way where much of the rich and detailed work and technical description provided in previous sections is rather abruptly wiped away with statements like "depiction of extreme molecular crowding and the ECM had to be compromised." While the decision itself is absolutely defendable, the reasoning behind it is not properly fleshed out. Given the technical expertise of the team, why not address such limitations by giving the user control of crowding parameters? This is offered as a potential next step in the 'Conclusion', but seems fundamental to a study that explores the difficult balance between what to show and what not to show. As noted above, this reviewer feels that this is partly due to the lack of a clear definition of what the authors consider a "user." Should the reader assume that this is a non-scientist – and if so, a student? At what educational level? Or is this a science museum goer? What are the specific goals of this experience? The authors mention wanting to promote "some cell biology fundamentals," but which ones specifically? Why is this set of visual experiences centered around breast cancer?

A more specific definition of 'user' would also help justify some of the important design decisions that were made both in terms of the representational choices (coarser protein meshes processed in Zbrush, and simplified lipid representations with a texture instead of meshes) but also the simplifications in dynamics of molecular actors (where the authors describe a process of developing 'MoA' animations based on coarse-grained simulations, but then using these only to inform more traditional 'rigging and animation' workflow typical of the entertainment industry). I would also have liked to read more specifics about how the different levels and environments in the visualization were connected together – how does the viewer navigate from one environment to another? Does this experience contains UI/interactive elements to do this beyond the freedom to gaze in any direction?

Finally, the authors do not touch at all on a discussion of how they would test the efficacy of their visualization on their target audience. While including such a protocol and carrying such work may not be part of this study and beyond the scope of the authors' initial project, I would recommend that they at least address how they would go about such an endeavor because the design challenges they describe and acknowledge throughout the paper cannot, ultimately, be 'guessed' – they have to be tested empirically.

Overall, the authors have accomplished an impressive amount of work and created what is no doubt a stunning experience. This is also evident in the quality of the images and figure design in the manuscript itself. The supplementary tables and figures are highly detailed and very useful to any who would seek to understand and recreate some of the technical aspects of this work. Although the authors discuss and lament the lack of a database beyond the PDB to share and annotate integrative modeling efforts like theirs, they do not follow-up by offering to share the models created in this study as a way to remedy such a problem. Nevertheless, I would recommend that this study be published in eLife as I believe it represents an impressive effort towards the advanced visualization of environments relevant to the readership. I would however ask that the authors address the points made above and summarized here for clarity here:

1. Please provide a clearer definition or discussion of your intended user.

2. For this user, what are the specific learning/communication objectives?

3. Draw more direct connections between these objectives and the specific design decisions that drove your project implementation.

4. Although carrying out an assessment study may be beyond the scope of this manuscript, please discuss how this could be carried out and, in doing so, provide a more explicit statement of learning goals and outcomes.

5. I would also want to know (and have the authors describe) whether they intend to share their models and the experience itself (since they say it can be set up on a user desktop – as opposed to their previous project which appears to require a more involved set-up for VR).

6. Finally, I find the suggestion in the conclusion that such environments may become useful for scientists to 'reflect upon their own data' is very interesting. However, such a statement requires some level of discussion addressing how visual design choices may need to change to accommodate the goals of this new kind of audience. Is there a difference in what molecular actors (or representational choices) could be left out or included, and what specific aspects of this interactive and immersive environment do the authors find most promising for use by the scientific community?

Reviewer #2:

This manuscript reports on the design considerations undertaken in the development of virtual reality cell explorer "Nanoscape". The authors describe in detail the process by which molecular entities and extracellular elements are sourced, modelled, distributed, and ultimately rendered. Detailed rationale is provided for the stylistic decisions both as it pertains to technological concerns and clarity of representation. For researchers working in the domain of molecular visualization this offers an interesting and insightful overview of this collaborative project.

I thoroughly enjoyed reviewing this manuscript, particularly because of the high level of detail and transparency provided by the authors with respect to the ways in which the environment they have created has been informed by empirical data. They describe the ways in which data has been modified and in each case provide rationale for decisions made (modifications or simplification of data as well as the use of artistic license for visual clarity). The supplemental materials too were very informative. This manuscript represents a standard for how all projects of this type should be documented and reported.

I have but one concern that is relatively very minor. It would be helpful if the authors provided some additional context as to who their users are? The users are mentioned several times throughout the manuscript as a consideration in design decisions made, but no clear characterization of the anticipated user group is ever offered.

In sum, I think this manuscript makes a valuable contribution to the domain of molecular visualization from both a technical and design perspective.

Reviewer #3:

This article discusses the use of computer visualization techniques to create an explorable 3D virtual environment, called Nanoscape, depicting a breast cancer cell within the context of a tumor.

1. Although the title suggests that the article will describe the Nanoscape application in detail, we were disappointed that only a cursory description of this project was given. While we understand that Nanoscape has yet to be released (and so user feedback should not be expected), we hoped to better understand what the purpose of the overall project is. Who is the target audience, how would the application be implemented, and what do the authors hope to achieve? An explanation of the novel features of Nanoscape (in comparison to other similar efforts) is particularly important given that many of the software techniques that have been used for this project have been previously released and/or described by others (e.g.Molecular Maya for protein modeling, CellPack for creating crowded environments) and do not represent new methods.

2. We were concerned that the article appears to be written primarily for 3D biomedical illustrators, rather than for research biologists. The article itself questioned whether 3D visualizations could support experimentalists (see lines 115-117). Given the readership of eLife, we felt that a much stronger consideration for the role of 3D visualization in the research sphere, and/or a more in-depth discussion of how these types of applications (and Nanoscape in particular) can impact public engagement and outreach is warranted.

3. In many sections of the article, the authors discussed general challenges faced when creating molecular environments, but did not discuss how they met these challenges in creating the Nanoscape models and how they made the decisions that they ultimately made. For example, the authors discussed the issue of conformational flexibility of proteins (paragraph starting on line 227) and the limitation posed when only one conformation of a protein has been structurally solved. How did the authors solve this problem in the case of Nanoscape? Likewise, when discussing the dynamics of lipids within a membrane, the authors point out that depicting the movement and heterogeneity of membranes is challenging, but did not describe how this challenge was met in the context of Nanoscape.

4. In line 202, mMaya is described as a software that "qualitatively replicate(s) molecular dynamics," and this software is described throughout the article as the main tool that was used to create dynamic animations of different proteins. A direct comparison of the process and computed trajectories produced by mMaya and those produced by established molecular dynamics simulation packages and/or coarse-grained modeling would ideally be provided to better support this description of mMaya, especially considering that mMaya is likely to be a software that is unfamiliar to many readers, and that there are (as far as we know) no other publications that describe the use of mMaya in the scientific literature.

Reviewer #1:

[…]

Overall, the authors have accomplished an impressive amount of work and created what is no doubt a stunning experience. This is also evident in the quality of the images and figure design in the manuscript itself. The supplementary tables and figures are highly detailed and very useful to any who would seek to understand and recreate some of the technical aspects of this work. Although the authors discuss and lament the lack of a database beyond the PDB to share and annotate integrative modeling efforts like theirs, they do not follow-up by offering to share the models created in this study as a way to remedy such a problem. Nevertheless, I would recommend that this study be published in eLife as I believe it represents an impressive effort towards the advanced visualization of environments relevant to the readership. I would however ask that the authors address the points made above and summarized here for clarity here:

1. Please provide a clearer definition or discussion of your intended user.

We more clearly define the primary user group as tertiary and university biology students. Secondary users have also been identified (public, experimental scientists), but are not the core target group.

2. For this user, what are the specific learning/communication objectives?

We more clearly define that the key objective of Nanoscape in this group is to support the learning of advanced biological concepts such as scale, density and interactions which may be misleading or oversimplified in other learning material. New Section in paper: Nanoscape Evaluation and broader applications

3. Draw more direct connections between these objectives and the specific design decisions that drove your project implementation.

Design decisions were paramount for both computational performance and aesthetic comfort. To avoid being visually overwhelmed, we had to reduce the number of proteins and small molecules (eg. water) in the environment. This also allowed a better appreciation of the 'vistas' of organic landscape and how several components interact with one another. In another example, the complexity of the ECM had to be simplified due to uncertainties about how the components co-ordinate their interactions with surface receptors. Proteins were stylised as polygonal meshes to highlight protein domains (some later animated), instead of atomic representation. In most cases, the appearance of the mesh of each component (protein/cell/blood vessel, etc.) was modified (made low-poly) to ensure real-time performance.

4. Although carrying out an assessment study may be beyond the scope of this manuscript, please discuss how this could be carried out and, in doing so, provide a more explicit statement of learning goals and outcomes.

We suggest that evaluation of the application for measures of usability (attention, cognitive demand etc) and educational value (as a didactic aid) is vital and is planned for phase 2 of this project. We would like to understand if Nanoscape, with its increased level of authenticity can help deepen the understanding of advanced topics such as protein density and molecular scale. Assessors can vary these parameters in application and evaluate the effect each has on basic and advanced cellular topics through exams. We would also like to test the effect of immersion and interactivity of this application against a more linear version of the content (like a pre-recorded video).

5. I would also want to know (and have the authors describe) whether they intend to share their models and the experience itself (since they say it can be set up on a user desktop – as opposed to their previous project which appears to require a more involved set-up for VR).

We will be making this publicly accessible (likely via the Steam store).

6. Finally, I find the suggestion in the conclusion that such environments may become useful for scientists to 'reflect upon their own data' is very interesting. However, such a statement requires some level of discussion addressing how visual design choices may need to change to accommodate the goals of this new kind of audience. Is there a difference in what molecular actors (or representational choices) could be left out or included, and what specific aspects of this interactive and immersive environment do the authors find most promising for use by the scientific community?

Have touched on the value to scientists – we believe that this application probably won’t offer a platform for rigorous data interrogation, but perhaps more holistic reflection of how systems integrate by seeing several modalities of data presented in the one cohesive format. Users may also gain an appreciation of the piece simply as an aesthetic piece of art or entertainment.

Reviewer #2:

[…]

I have but one concern that is relatively very minor. It would be helpful if the authors provided some additional context as to who their users are? The users are mentioned several times throughout the manuscript as a consideration in design decisions made, but no clear characterization of the anticipated user group is ever offered.

Please see reviewer 1, comments 1-2.

Reviewer #3:

This article discusses the use of computer visualization techniques to create an explorable 3D virtual environment, called Nanoscape, depicting a breast cancer cell within the context of a tumor.

1. Although the title suggests that the article will describe the Nanoscape application in detail, we were disappointed that only a cursory description of this project was given. While we understand that Nanoscape has yet to be released (and so user feedback should not be expected), we hoped to better understand what the purpose of the overall project is. Who is the target audience, how would the application be implemented, and what do the authors hope to achieve? An explanation of the novel features of Nanoscape (in comparison to other similar efforts) is particularly important given that many of the software techniques that have been used for this project have been previously released and/or described by others (e.g.Molecular Maya for protein modeling, CellPack for creating crowded environments) and do not represent new methods.

Please see reviewer 1, comments 1-2.

2. We were concerned that the article appears to be written primarily for 3D biomedical illustrators, rather than for research biologists. The article itself questioned whether 3D visualizations could support experimentalists (see lines 115-117). Given the readership of eLife, we felt that a much stronger consideration for the role of 3D visualization in the research sphere, and/or a more in-depth discussion of how these types of applications (and Nanoscape in particular) can impact public engagement and outreach is warranted.

Please see reviewer 1, comment 6.

3. In many sections of the article, the authors discussed general challenges faced when creating molecular environments, but did not discuss how they met these challenges in creating the Nanoscape models and how they made the decisions that they ultimately made. For example, the authors discussed the issue of conformational flexibility of proteins (paragraph starting on line 227) and the limitation posed when only one conformation of a protein has been structurally solved. How did the authors solve this problem in the case of Nanoscape? Likewise, when discussing the dynamics of lipids within a membrane, the authors point out that depicting the movement and heterogeneity of membranes is challenging, but did not describe how this challenge was met in the context of Nanoscape.

In many cases, proteins existed only in one conformational state, so molecular animations were not possible for those. This application did not intend to offer rigorous molecular dynamics simulations, as this was outside the scope of the work. Where several protein states existed, we were able to animate those proteins with a reasonable level of confidence. Another option that we did not explore was to simply use artistic licence and artistically manipulate conformational changes to suggest flexibility of the proteins, but this was not completed for the current version of the application. We also note that due to performance reasons, we elected to simulate the cell membrane as an animated texture instead of individual lipids.

4. In line 202, mMaya is described as a software that "qualitatively replicate(s) molecular dynamics," and this software is described throughout the article as the main tool that was used to create dynamic animations of different proteins. A direct comparison of the process and computed trajectories produced by mMaya and those produced by established molecular dynamics simulation packages and/or coarse-grained modeling would ideally be provided to better support this description of mMaya, especially considering that mMaya is likely to be a software that is unfamiliar to many readers, and that there are (as far as we know) no other publications that describe the use of mMaya in the scientific literature.

Have included a detailed description of mMaya from its publishers in the supplementary figures.

Author details

1. Shereen R Kadir

   Shereen R Kadir is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3960-988X

2. Andrew Lilja

   Andrew Lilja is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Data curation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8311-3702

3. Nick Gunn

   Nick Gunn is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Software, Methodology

   Competing interests

   No competing interests declared

4. Campbell Strong

   Campbell Strong is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Software, Methodology

   Competing interests

   No competing interests declared

5. Rowan T Hughes

   Rowan T Hughes is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Software, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5618-381X

6. Benjamin J Bailey

   Benjamin J Bailey is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

7. James Rae

   James Rae is in the Institute for Molecular Bioscience, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, and Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, Australia

   Contribution

   Visualization

   Competing interests

   No competing interests declared

8. Robert G Parton

   Robert G Parton is in the Institute for Molecular Bioscience, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, and Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, Australia

   Contribution

   Conceptualization, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7494-5248

9. John McGhee

   John McGhee is in the 3D Visualisation Aesthetics Lab, School of Art and Design, and the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of New South Wales, Sydney, Australia

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing

   For correspondence

   john.mcghee@unsw.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9264-7535

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