extfieldsofstudy:
Computer Science
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text:
Hindrances to precise recovery of cellular forces in fibrous biopolymer networks

How cells move through the three-dimensional extracellular matrix (ECM) is of increasing interest in attempts to understand important biological processes such as cancer metastasis. Just as in motion on flat surfaces, it is expected that experimental measurements of cell-generated forces will provide valuable information for uncovering the mechanisms of cell migration. However, the recovery of forces in fibrous biopolymer networks may suffer from large errors. Here, within the framework of lattice-based models, we explore possible issues in force recovery by solving the inverse problem: how can one determine the forces cells exert to their surroundings from the deformation of the ECM? Our results indicate that irregular cell traction patterns, the uncertainty of local fiber stiffness, the non-affine nature of ECM deformations and inadequate knowledge of network topology will all prevent the precise force determination. At the end, we discuss possible ways of overcoming these difficulties.

I. INTRODUCTION
The migration of eukaryotic cells in complex environments plays a significant role in many biological processes, such as embryonic morphogenesis, immune defense, and tumor invasion [1]. One widely encountered biomechanical environment for migrating eukaryotic cells in vivo is the three-dimensional (3D) extracellular matrix (ECM), composed of a dense network of biopolymers such as collagen and fibrin [2]. To make their way through ECM, cells apply a variety of different strategies, involving mechanisms of cytoskeleton force generation, protease production and cell adhesions. Whatever strategy cells use, cell-generated forces acting on the ECM are valuable clues to infer what is happening within a migrating 3D cell.
Recently, experimental advances have been made in quantifying the ECM's response to migrating cells [3][4][5][6]. In these experimental setups, cells are often cultured in artificially synthesized extracellular matrix (ECM), such as type I collagen gel, which efficiently mimics the environment in living tissues [7]. As the cells migrate, they deform the surrounding environment; this deformation is trackable by, for example, placing marker beads in the gel or imaging collagen itself. However, from a theoretical perspective, a gap still exists between knowing the deformation of the ECM and determining what forces cells have exerted on that ECM. The inversion from the former to the latter remains elusive, because the ECM displays very complex properties such as strain-stiffening [8] and non-affine deformation [9].
In recent work, Steinwachs et al attempted a reconstruction scheme based on a continuous elasticity model, which phenomenologically captures the strain-stiffening property of collagen gels [6]. This effort goes beyond previous approaches which used linear elastic assumptions and hence represents a significant step forward [3,4,10]. However it remains unclear how accurately this method would capture the mechanics of a real biopolymer network. In particular real networks are expected to exhibit micromechanical fluctuations [11] in its properties on the scale of the network elements which are not very different than that of the embedded cell. Therefore, it is possible that a more detailed understanding of ECM networks is essential for a quantitatively successful reconstruction of cellular forces in ECM.
Here, we use a lattice-based mechanical model to study this force reconstruction problem. In particular, we make use of recent progress in the soft-matter physics community towards an understanding of the ECM system [12]. It has been shown that these systems can be modeled as a disordered network of semiflexible polymers with an interplay between bond stretching and bending. On the basis of this idea, computational models have been built to capture the critical properties such as strain stiffening, negative normal stress, and non-affine deformations. These models roughly fall into two categories: latticebased models [13][14][15][16][17][18][19][20][21] and off-lattice models [21][22][23][24][25][26][27][28][29] . The former places straight fibers on a regular lattice; these fibers are determined by straight segments of bonds on a diluted network. The other approach consists of placing stochastically positioned fibers, intersecting with each other and forming crosslinks; this is usually referred to as a Mikado model. As in both of these cases the mechanics is controlled by critical behavior [13,19,21] around the Maxwell point (at which bending becomes the dominant response mechanism at small strain), the results from these different approaches are extremely consistent with each other [30,31].
In this paper, we study the general reconstruction problem based on a two-dimensional (2D) diluted lattice model. We choose the lattice model for its computational efficiency, The fact that lattice-based models in 2D and 3D capture similar non-affine behavior and nonlinear elastic response of ECM [13], allows us to quantitatively explore the feasibility of the force reconstruction without a need to carry out full 3D simulations. Our approach enables us to study the feasibility of doing this reconstruction even if we do not know the exact microstructure of the material. We find several factors that prevent precise recoveries of cellular forces, including the irregular cell traction pattern, the uncertainty of local fiber stiffness, the non-affine nature of ECM and inadequate knowledge of network topology. Our findings sug-gest continuum theories adopted in most existing traction force microscopy (TFM) methods may generate large inaccuracies in force estimation. At the end of our paper, we discuss how future studies may be able to correctly characterize micromechanical information and give more accurate force predictions.

II. MODELS AND SIMULATIONS
We model the ECM as a diluted triangular lattice (Fig  1a). In this lattice, each bond, with stretching stiffness k and bending stiffness κ , exists stochastically with a probability p. The Hamiltonian is: in which a is the rest length of each bond. g ij = 1 if the bond between node i and j is present and g ij = 0 if the bond is removed. The first term refers to the stretching energy: < i, j > sums over all neighboring lattice sites and |R ij | is the length of bond in the deformed state. The second term represents the bending energy: < i, j, k > sums over all groups of three co-linear consecutive lattice sites in the reference state and ∆θ ijk is the change of angle in the deformed state. In lattice models, p satisfies pZ = z , Z is the coordination number, which is 6 for a triangular lattice, and z is the average connectivity of the biopolymer network. Since experiments have shown z ≈ 3.4 [32], we study p in the range [0.5, 0.65] and use p = 0.57 to illustrate our findings. The same p value was adopted in our previous study [11], and it predicts micromechanical properties consistent with experimental observation.
We insert a round cell into the network by cutting a circular hole in the middle (Fig 1b). The cell and the network are connected by the attached nodes, which are the intersection points between the cell boundary and network bonds in the reference state. Then we let the cell stretch by radially displacing attached nodes; for the i th attached node, its displacement towards the cell center satisfies: in which θ i is the angular position of the i th node, the vector P = (A 0 , A 1 , ...A N , B 0 , ..., B N ) are the determining parameters of cell stretching and the length of P determines the number of degrees of freedom. Note that we use cell contraction to generate motion of the points where the cell is attached to the surface; exerting this displacement is equivalent to putting traction forces on the attached nodes, since the Hamiltonian of our simple model is known. In real biological systems, the Hamiltonian may not be known in detail, which of course causes difficulties in modeling the system. In this paper, we address the other aspect of the problem, namely what effects might prevent the successful inference of the traction forces from marker beads displacements even given that we have a models that accurately describes the system.
Once the cell is deformed, we relax the network into its energy minimum state by the conjugate gradient method. Each parameter setting P leads to a particular cell stretching pattern: which results in ECM deformation. In our scheme, we assume that ECM deformation is measured through the displacements of a set of M marker beads, as following: We stochastically set the parameters P = P set and generate the corresponding "observed" displacements of marker beads, D observed . By minimizing we can realize the force reconstruction by approximating the P set with P guess . To minimize the function f , we apply Particle Swarm Optimization (PSO) [33].

Intrinsic Limitations in Reconstruction
We carry out our simulations in a 60 x 60 lattice, with p ∈ [0.50, 0.65]. To check the possible boundary effects of the lattice, we have verified that a 100 x 100 lattice gives similar results. We set the lattice spacing to be a and our round cell has an initial radius R 0 = 8a. Physically, a corresponds to the persistent length of collagen fibers ( ≈ 1µm). Suppose the cell contraction can be characterized by the N longest wavelength modes: N ), and ξ i are stochastic variables uniformly distributed between 0 and 1. A constant a 0 ( usually around 2a to 3a in our simulations) is added to make sure that the cell mostly contracts, as most cells studied in such ECM systems are contractile. To track the ECM deformation, we initially let all lattice sites be inhabited by marker beads. As mentioned above, we solve for the stretching pattern that leads to the observed bead displacements with Particle Swarm Optimization algorithm.
Since the Particle Swarm Optimization algorithm is a heuristic algorithm, instead of giving a deterministic answer, we measure how likely is it that the reconstruction will succeed. Here, we define successful reconstructions as predictions for all traction forces of all attached nodes deviating by no more than 5% in magnitude and 5 degrees in direction from the input data . Of course, the chances to succeed for a PSO procedure depends on the number of searching rounds and ideally one can always get to the right answer with infinite computer time. Due to limited resources, we assign at most three rounds of PSO searches, each with a maximum of 100 steps.
We ran groups of 50 simulations for a variety of N values. The results indicate the existence of a limit of resolution in force reconstructions for spatial frequencies. The reconstructions are mostly successful if the cell traction input is restricted to the 3 or 4 longest wavelength modes. Conversely, the algorithm experiences a sharp drop in its performance when higher frequencies are present (Fig  2a). That is to say, a highly non-uniform cell contraction can reduce the resolution of both the magnitude and the direction of the reconstructed forces (Fig 2b-g). It may be surprising that the minimization of an error function in as few as 10 dimensions cannot converge to the global minimum, but the problem here is highly nonlinear, i.e. the cell contraction will be transformed into marker beads displacements in a highly nonlinear way. When high-frequency modes are present in the cell contraction, the landscape of the error function f becomes highly rugged (Fig 3), which traps our optimization algorithm in local minima. Sometimes (as is the case in Fig. 2) the reconstruction can still recover information regarding the low-order modes even while failing to accurately determine the higher-order ones. Other times, as for example in Fig. 4, even the low-order modes are estimated incorrectly. Our results indicate, therefore, that only relatively smooth force profiles can be reliably recovered, Next, we consider the effect of errors in the experimental measurements. We stochastically disturb the positions of all marker beads after the network has relaxed to its energy-minimized state by adding Gaussian noise. We then redo the inverse problem. We found a reasonable degree of robustness in the reconstruction, with accurate forces found even with the variance of the added noise as large as 30% bond length (see Fig 5a). Another possible source of error is the inaccurate estimate of the parameters in the Hamiltonian. Accordingly, we vary the stretching and bending stiffnesses of our system and see how it influences the traction force calculation. As shown in Fig 5b, our scheme fails as soon as stretching stiffness varies by more than 10%, indicating the traction force recovery is very sensitive to fiber stiffness measurements.
We have discussed the role of cell contraction pattern and measurement errors in our general scheme of the traction force reconstruction. In these results, we have assumed that we can measure the displacements of all nodes in our network. In other words, we are considering the high marker beads concentration limit, where we have complete information of the network deformation. A straightforward question that arises here is how the bead marker distribution affects the force reconstruction pre- cision. To answer this question, we start by "turning off" the marker beads far away from the cell center ( > R max ). Noise-free simulations with R max = 10a, 15a, 20a, 25a all lead to perfect performance, indicating that the full information of network deformation is redundant. To further investigate the effects of the distribution of marker beads near the cell, we only keep the marker beads within a range of R min to R max from the center of the cell. As Table 1 shows, the reconstruction performance drops sharply as the marker beads are located further than 15 lattice spacings from the cell, which suggests information loss in long-distance force transmission. That is to say, the beads closer to the cell contains more information regarding cellular forces. In addition, inversion attempts with both randomly and regularly diluted (up to 50%) distributions of beads in the neighborhood of the cell show similar performance (data not shown), showing the insensitivity of the reconstruction to details of the local bead distributions. So far, all our studies focus on the contraction of round cells. In reality, however, cells often display long protrusive structures. As a simple extension of our approach, we have also studied elliptical cells (Fig6 (a,b)). We allow an elliptical cell to translate and to shrink along its long axis. In this case, our simulations can quickly converge to the correct reconstruction. (Fig 6(c,d,e)). Noticing that slight tangential movements are also possible, we also verify that the reconstruction works well for a uniformly rotated round cell ( Fig 7(a, b) ).

Roles of Non-affinity and Micromechanics
As we have discussed above, the response of the ECM to cell-induced deformation is highly non-linear and nonaffine. This is ultimately due to the fact that the network lies well below the Maxwell point and hence exhibits a transition from bending-dominated to stretchingdominated as strain increases and the fibers become more aligned. The other significant feature is that the ECM, both in our model and in experiments, is highly heterogeneous on the cellular scale. These features argue against being able to accurately reconstruct cell contractions by spatially-uniform linear elastic models. To investigate the seriousness of the problem, we first compare our results with calculations on a full triangular lattice, with all bonds present except for the ones attached to the cell, representing an approximately affine model. To make a fair comparison, we have to relate the parameters of this system with our diluted lattice model. Both the bulk modulus and shear modulus of the full lattice are solely determined by stretching stiffness k, since the deformation of the full lattice is affine and the bending energy is always zero. We tune the value of k to match the bulk modulus of the full lattice with that of the dilute lattice.
(Alternatively, we can match the shear modulus, which leads to similar conclusions.) In a forward comparison, we contract our cell with exactly the same pattern in a diluted lattice and in a full lattice and compare the displacements of marker beads in the range between ten and fifteen lattice spacings from the center of the cell. As seen in Fig 8(a-c), significant differences in the network responses are observed between the non-affine case (p ∼ 0.6) and the affine case (p = 1).
In the reconstruction comparison, we relax our system on a diluted lattice so as to locate the marker beads and then attempt to recover the cell deformation based on a full lattice (Fig 9(a,b) ); this would be analogous to doing the inverse problem with a linear elastic model. In all of our 50 attempts, reconstructions fail with large deviations (Fig 9 (c,d,e) ). Both the forward and backward calculations imply that not surprisingly, the non-affinity of ECM is critical in the force transmission. Simple affine models mischaracterize the system and lead to highly inaccurate predictions.
Finally, we turn to the role of micromechanics, namely the local variation of elastic response properties that of course correlate with the precise local network structure. Even if an ECM model captures the non-affinity aspect properly, the precise network topology can induce notable differences in the network deformation. To further elucidate the idea, we apply similar forward and backward comparison between two networks of the same p value but different network topology. In the forward comparison, deformations may differ by a magnitude about twice the lattice spacing in these two cases for the same cell contractions (Fig 10a). As for the backward comparison, we relax the network within one network, while reconstructing cell contraction based on a differently connected network with the same macrscopic properties. All simulations, without exception, lead to remarkably inaccurate predictions in both force magnitude and direction (Fig 10(b,c) ). An immediate consequence is that a nonlinear continuum model would be incapable of correctly describing the deformation field of cellularized networks, since network topology detail is missing in any continuum model. Therefore, we need much more information about network structure to successfully reconstruct traction forces.

DISCUSSION
In this study, we explore how the mechanical complexity of the ECM material limits our ability to invert ECM deformations to obtain traction forces. To be more specific, we have explored the feasibility of reconstructing cell-generated traction forces from ECM deformation based on a general discrete ECM model. Our model is based on a 2D diluted triangular lattice, which has been shown to mimic both macroscopic properties of rheology such as strain-stiffening and alignment, and local heterogeneities on the scale of individual cells of real 3D ECMs [11]. Moreover, the lattice model in this study is able to capture the buckling effects of single fibers, as discussed in Ref [31]. We limit our investigation in 2D in this study for the sake of computational efficiency. The extension to a 3D model is straightforward and it should exhibit similar physics.
Within our model, we find that there is an intrinsic limitation in the spatial resolution of cell contraction. This limitation is not too surprising. We can imagine a pair of closely neighboring adhesion sites on the cell membrane which undergo very different amounts of stretching; if they exchange their stretching, almost the same displace- ments of marker s will be observed, thus it is hard to distinguish the different configurations in the reconstruction. It explains why a relatively smooth stretching configuration, where closely neighboring bonds stretch similarly, will suffer much less from this degeneracy. In addition, we find that the inaccurate estimates of stretching stiffness or bending stiffness of fibers can lead to imprecise predictions of traction forces. Note that we assume constant fiber stiffness in our network; In reality, however, each fiber has different thickness and length, and fiber stiffness varies from fiber to fiber, which further hinders the accuracy of traction force reconstruction.
By comparing affine networks (p ∼ 0.6) and non-affine networks using a forward and a backward approach, we find that the non-affinity of networks can fundamentally change the way cell deforms ECMs. Furthermore, even two networks with a same p value but different topology can exhibit remarkable differences in both forward and backward comparisons. These results strongly suggest that continuum models, which leave out microscopic structures and heterogeneity of ECMs, are not appropriate tools in inverting traction forces from ECM deformations. It also suggests that the inversion must make use of direct measurements of local network structure as op-  To overcome all the hindrances to precise recoveries of traction forces, we suggest that the only feasible option is to couple local active local measurements to the inversion algorithm. These measurements can be implemented by actively perturbing the beads located in a variety of regions of the ECM, as was done for example in a recent study of ECM micromechanics [11]. With observations of the mechanical responses for each of these perturbations observed, we believe that the micromechanical information can be sampled well enough to reconstruct a network model for that specific piece of ECM and thereby recover precisely the forces the cells are exerting on that ECM. We will leave an attempt to verify this approach to future work.


extfieldsofstudy: Chemistry Medicine

text:
SOD1 stimulates lamellipodial protrusions in Neuro 2A cell lines

ABSTRACT We here investigated the effects of overexpressed superoxide dismutase (SOD)1 and amyotrophic lateral sclerosis (ALS)-linked SOD1 mutants G93A and G147S in Neuro 2A (N2A) cell lines, and found a three-fold increase in lamellipodia either in cells cultured under differentiated or undifferentiated growth conditions. In undifferentiated N2A cells, SOD1 constructs promoted lamellipodial protrusions to similar extent as the overexpression of Rac1, and SOD1-mediated lamellipodia were prevented by coexpression of the N17 dominant-negative form of Rac1, or shRNA for a downstream effector of Rac1, the insulin receptor tyrosine kinase substrate p53 (IRSp53) or its binding partner LIN7. Moreover, no additive effect was measured by coexpression of the SOD1 constructs with Rac1, IRSp53 or LIN7. Collectively these data support a role for SOD1 in the regulation of Rac1-mediated lamellipodia pathway, a property fully retained by the two SOD1 mutants.

Introduction
Amyotrophic lateral sclerosis (ALS) is a devastating agerelated neurodegenerative disease characterised by a progressive loss of motor neurons of the central nervous system, leading to progressive muscle denervation and paralysis [1]. No effective therapy is available for ALS, and understanding the disease pathogenesis could help in developing effective treatments. The majority of ALS cases are sporadic while familial forms account only for 10% of all ALS [2]. Twenty percent of inherited ALS is caused by mutations in the gene encoding for superoxide dismutase 1 (SOD1), an ubiquitously expressed enzyme functioning in the clearance of potentially toxic superoxide radicals. The resulting mutant proteins have additional but still unclear functions that are toxic for motor neurons. A non-cell autonomous mechanism involving contiguous and functionally related glial cells has also been implicated in the disease [3]. Whereas damage to motor neurons is associated to the onset of ALS, damage to astrocytes and microglia severely accelerates disease progression [4][5][6].
Toxicity of mutant SOD1 has been associated with overactivation of NADPH oxidase (NOX2), due to an apparent higher affinity of mutants to the Rac1 member of the Rho GTPase family, compared to the wild-type (WT) SOD1, which in turn would lock NOX2 in its active superoxide-producing form even in oxidising conditions when the WT form dissociates from Rac1 [7]. Rac1 is, however, known to play a major role in the regulation of the actin cytoskeleton inside lamellipodia, the actin-based membranous projections that usually appear on moving edge of any cell type [8] and that are fundamental for neurite formation and collateral branches outgrowth [9]. We therefore analysed whether SOD1 may promote lamellipodial protrusions, and whether this property is retained by ALS-associated mutated SOD1. The SOD1 mutants used in this study are the well characterised G93A mutant, which is known to retain the dismutase catalytic activity of the WT enzyme [10], and the less studied G147S, lacking of the catalytic activity and causing a very rapid clinical course of the disease [11]. The effects of SOD1 overexpression were investigated in Neuro 2A (N2A) cells, a mouse neural crest-derived cell line that exhibit fibroblast-like morphology when cultured in regular medium supplemented with 15% serum, while differentiate into neuronal-like cells within a few days when cultured in serum-deprived medium [12].
The effects of SOD1 overexpression on lamellipodia formation were quantitatively analysed by phalloidin immunostaining to visualise the typical F-actin staining inside lamellipodia. The SOD1mediated pathway for lamellipodia was investigated by coexpression experiments with WT or a dominant negative Rac1, and by interfering with the expression of a downstream effector of Rac1, the insulin receptor substrate of 53 kDa, IRSp53 [8,13], and its binding partner LIN7 [14]. In addition, to verify the effect of SOD1 overexpression in neuronal differentiation, the lamellipodia promoting activity of SOD1 constructs was also investigated in N2A cells cultured under serum-free conditions.

Results
Overexpression of SOD1 mediates Rac1-dependent lamellipodia extension in neuronal-like N2A cells To test whether SOD1 could induce the formation of membrane protrusions in neuronal cells, we ectopically overexpressed untagged WT human SOD1 or the ALSlinked G93A and G147S mutants in N2A cells. Approximately a 8 fold level of expression over the endogenous was measured for each transfected SOD1 (see Figure S1). In addition, as negative and positive controls, the cells were transiently transfected with GFP fused to a plasma membrane localisation signal (mGFP) and Rac1 fused to HA, respectively. Cells cultured in undifferentiated growth conditions (15% FBS) for 48 hours after transfection were scored for the presence of lamellipodial protrusions by phalloidin staining of F-actin.
In these culture conditions, N2A cells have either a spherical or fibroblast-like morphology, and extend lamellipodia and filopodia in order to sense and sample the surrounding environment. Localisation of SOD1 as well as Rac1 in F-actin enriched lamellipodia was clearly revealed by indirect double immunofluorescence staining with the specific antibodies ( Figure 1(A)).
We next compared the lamellipodia-inducing capacity of SOD1 with that of Rac1, and analysed the effect of their cotransfection (Figure 1(B)). Approximately a three-fold increase in the percentage of lamellipodiabearing cells was found in Rac1-transfected cells (~52%) compared to mGFP transfected cells (~17%). The overexpression of SOD1, either WT or mutants, promoted a similar increase in lamellipodia than Rac1 (~47%). The same effect measured in cells expressing Rac1 alone was obtained in cells coexpressing Rac1 with each of the SOD1 constructs (~52%), suggesting that SOD1 and Rac1 are in the same pathway.
In order to verify this possibility, Rac1 was inhibited by expression of a dominant negative mutant, the HAtagged Rac1-N17 (Figure 1(B)). As expected, inhibition of Rac1 activity decreased the number of cells with lamellipodia under 10% in control mGFP-cells, and, most importantly, the percentage of lamellipodia-bearing cells remained at control levels also in SOD1-cotransfected cells (~13,5%), thus suggesting that SOD1 is unable to stimulate lamellipodia when Rac1 activity is inhibited.
In line with data in literature indicating a main role for Rac1 activation in the formation and function of lamellipodia rather than filopodia, we did not detect any increase in filopodia in cells expressing SOD1 and/ or Rac1 (data not shown).
The IRSp53-LIN7 complex is a downstream effector of SOD1-mediated extension of lamellipodia We next tested SOD1 involvement in Rac1-mediated pathway for lamellipodia formation by altering the expression of a downstream effector of Rac1, the insulin receptor substrate protein of 53 KDa (IRSp53, also named BAIAP2), which acts as a link between Rac1 and WAVE-ARP 2/3 complexes in signalling lamellipodia formation [8,13].
The IRSp53 protein contains multiple domains for protein-protein interactions, including a PDZ domainbinding motif at its C-terminus for interaction with the scaffold protein LIN7 (also named MALS/Velis), and LIN7 association is required to positively regulate IRSp53 activity at specific membrane sites in epithelial and neuronal cells [14][15][16][17]. We therefore tested whether IRSp53 and LIN7 play a role in SOD1-dependent formation of lamellipodia by analysing the percentage of lamellipodia-bearing cells in SOD1 overexpressing N2A cells downregulated for/coexpressing IRSp53 or LIN7 (Figure 1(C-D)).
SOD1-mediated increase in lamellipodia was greatly prevented in N2A cells downregulated for IRSp53 or LIN7C, the LIN7 form mainly expressed by these cells [16]. In line with a role for the IRSp53-LIN7 complex in lamellipodia formation, the percentage of SOD1expressing cells with lamellipodia (~45%) dropped tõ 21% and~17% in cells downregulated for IRSp53 or LIN7, respectively. Moreover, the overexpression of IRSp53 or LIN7 promoted a > two-fold increase in the percentage of cells with lamellipodia (from 17% tõ 40%), and no further increase was observed in cells coexpressing IRSp53 or LIN7 with any of the SOD1 constructs (Figure 1(D)).
Once again we did not observe a different behaviour among SOD1 WT and the mutants, thus indicating that these mutants retain their function in promoting lamellipodia.

Overexpression of SOD1 stimulates formation of lamellipodia in N2A cells cultured under differentiated growth conditions
In order to evaluate the effect of SOD1 expression on membrane dynamics in differentiated N2A cells, 24 hours after transfection cells were cultured for additional 24-48 hrs in serum-deprived media (-FBS) prior to fixation and immunofluorescence staining for F-actin and SOD1 to analyse lamellipodial protrusions in transfected cells (Figure 2).  The quantitative analysis indicated significant higher lamellipodia-inducing activity in N2A cells transfected with SOD1, either WT or mutants, compared to control mGFP transfected cells: upon serum deprivation for 24 or 48 hrs, the percentage of cells with lamellipodia dropped to under 10% in mGFP transfected cells, while it remained high (~40%) in SOD1-transfected cells. Moreover, in these cells, lamellipodia occupied one third of their entire perimeter (Figure 2(A)).
N2A cells expressing mGFP showed a morphology typical of differentiated neuronal cells (Figure 2(B)), characterised by few short and one longer neurite emerging from the cell body, with occasional lamellipodia restricted to their extremity (growth cone). This cell morphology was significantly altered in SOD1 transfected cells: processes of similar length were emerging from the cell body and lamellipodia were not restricted to their growth cones, but rather unusually found all along neurite shafts. These alterations were virtually never observed in mGFP expressing cells, while they were clearly detected also in cells overexpressing all SOD1 constructs. The altered morphology observed in these cells suggest that overstimulation of lamellipodial pathways might be detrimental to neuronal differentiation.

Discussion
This study is based on data in literature reporting that wild type SOD1 and some ALS-mutated SOD1 forms bind and stabilise the Rho GTPases Rac1 [7]. In glial cells, the authors identified NOX2 as a target of Rac1 activity promoted by SOD1. However, Rac1 activity is mainly involved in the organisation of actin-based lamellipodial protrusions in all the cell types, with pivotal roles in neurons, where these structures are required for axon extension and guidance, formation of axon branches and synaptic structures [9]. We therefore hypothesised that overexpression of SOD1 and ALS-associated mutated SOD1 would promote lamellipodial protrusions in neuronal cells. This hypothesis was tested in N2A cells cultured under undifferentiated and differentiated conditions, and in both cell culture conditions we found that the expression of SOD1 promoted a three-fold increase in lamellipodia, identified by their typical morphology of sheet-like membrane protrusions enriched in F-actin.
The involvement of Rac1 in the SOD1 mediated pathway for lamellipodia is inferred by results obtained in undifferentiated N2A cells. In these cells we found that: i) overexpressed SOD1 stimulated lamellipodia to similar extent than overexpressed Rac1; ii) SOD1-mediated lamellipodia were prevented by inhibition of Rac1 pathways (by coexpression of the dominant negative Rac1 N17 or in cells downregulated for a downstream effector of Rac1, the IRSp53-LIN7 complex); (iii) no further increase in lamellipodia was observed in SOD1 cells coexpressing Rac1, IRSp53 or LIN7.
Collectively these data support the possibility that the increase in lamellipodia observed in the surface of cells overexpressing SOD1 is a consequence of SOD1 activation of Rac1-mediated pathway for lamellipodial extension. Harraz and colleagues [7] have previously shown that wild type SOD1 and mutants L8Q and G10V (both lacking their catalytic activity) bind and activate Rac1, our data suggest that this also occurs for the G93A and G147S mutants. In line with the possibility that also these SOD1 mutants bind and activate Rac1, the similar increase in lamellipodia found in cells overexpressing G93A and G147S is not surprising. However, reduced rather than increased levels of activated Rac1 (Rac1GTP) were found in cell lysates of SH-SY5Y neuroblastoma cells overexpressing SOD1 mutants G93A and H80R [18]. The decrease in activated Rac1 may be due to differences in neuronal cell type, alternatively, the lamellipodia measurement used in our study may better reflect Rac1 activity on the cell surface than total Rac1GTP measured by pull down assays.
The overactivation of lamellipodial pathways was observed either by overexpression of WT or mutated forms of SOD1 and was associated to morphological defects during neuronal differentiation in N2A cells, suggesting a novel mechanism of SOD1-mediated neurotoxicity. Regulatory mechanisms normally preventing toxicity of WT SOD1 at basal expression levels might be unable to operate in conditions of overexpression. In line with this possibility, neuronal tissue abnormalities have been reported in WT SOD1 transgenic mice [19], and abnormally high levels of SOD1 transcripts were reported in spinal cord, brain stem and lymphocytes of sporadic ALS patients [20], although it remains to be investigated whether these conditions are associated to overstimulation of Rac1-mediated pathway for lamellipodia.

Constructs
The constructs GFP-IRSp53, GFP-LIN7A and shRNA LIN7C have been previously described [15,16]; shRNA IRSp53 was purchased from Sigma Aldrich. A 50% reduction of LIN7 was achieved by shRNA LIN7C [16], while a slightly lower downregulation of IRSp53 was detected by Western blot in cells transfected with shRNA IRSp53 ( Figure S2). cDNAs for human SOD1 and SOD1G93A were a kind gift of Dr. C. Bendotti [21]. SOD1 G147S was generated by means of Quickchange Site-directed Mutagenesis (Stratagene, CA) polymerase chain reaction (PCR) using the hSOD1 cDNA as the template. The fulllength cDNA insert was excised and recloned into the BamHI and NotI sites of the mammalian expression vector pcDNA3 (Invitrogen, Paisley, UK). The absence of unwanted substitution was checked by sequencing.
The GFP construct fused to a membrane localisation sequence (mGFP) was a kind gift from Dr N. Borgese [22]. pCDNA3-Rac1-HA and pCDNA3-Rac1N17 were a kind gift of Dr. G. Scita.

Cell culture and transfection
Murine neuroblastoma Neuro2A (N2A) cells [23] were grown in DMEM with 10% foetal bovine serum (FBS), 1 mM glutamine and antibiotics. The cell lines were cultured in a 37°C incubator containing 5% CO 2 . Transfections: cDNAs and shRNAs were transiently transfected in N2A cells by using PEI (Invitrogen, Carlsbad, CA), following the manufacturer's protocol. To maintain the same dosage of transfected SOD1 in all the experiments, SOD1 cDNA has been always cotransfected with control empty vectors or with the cDNA of interest. Twenty-four hours after transfection cells were cultured for additional 24 or 48 hours in media containing 15% FBS (undifferentiated N2A), or in regular media depleted of serum (-FBS) for 24 or 48 h (differentiated cells) before paraformaldehyde fixation.

Antibodies
The polyclonal rabbit anti-LIN7 antiserum was raised against the histidine-LIN7A fusion protein [15]; the polyclonal rabbit anti-IRSp53 was a kind gift from Dr E. Kim (Korea Advanced Institute of Science and Technology) [24]. Commercial primary antibodies: sheep polyclonal against human SOD1 (Calbiochem, Darmstadt, Germany) and mouse monoclonal anti-HA antibody (Invitrogen, Paisley, UK).

Immunofluorescence
After being grown and transfected as described, the cells were fixed for 20 min in 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) and permeabilised with 0.5% Triton X-100. Immunostaining with primary antibodies was followed by incubation with Fluorescein isothiocyanate (FITC)/CY5 anti-rabbit/mouse antibodies (Jackson ImmunoResearch). Texas-red Phalloidin was used to stain F-actin (Molecular Probes). Wide-field microscopy was performed with a Zeiss Axioplan inverted phase-contrast microscope (60x objective) connected with an AxioCam HRm CCD camera.

Image and statistical analysis
For quantification of the percentage of cells-bearing lamellipodia: The percentage was manually quantified using a Zeiss Axioplan inverted phase-contrast microscope (60× objective) connected with an AxioCam HRm CCD camera. Lamellipodia were defined as ruffled plasma membrane processes containing brushlike actin filaments. This definition in no way attributes any functional value to these structures and is purely a reflection of morphological similarity to lamellipodia. For each condition and transfectant, data are the mean ± s.e.m. of 150 cells examined in randomly chosen fields from 3 independent experiments.
For quantification of the percentage of cell perimeter occupied by lamellipodia, wide-field images of transfected N2A cells were analysed with ImageJ as follows: the full perimeter of each cell was measured with the "segmented line" tool. Lamellipodia of the same cells (identified by both the typical ruffled appearance of the plasma membrane and the brush-like appearance of F-actin) were then outlined and measured with the same tool. For each condition and transfectant, data are the mean ± s.e.m. of 60 cells from 3 independent experiments.
All quantitative data are presented as means ± s.e.m.; multiple comparisons among groups were carried out with Student's t-test using Prism software (GraphPad PrismTM software).


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Cellulose modification by recyclable swelling solvents

Background The invention of efficient systems for lignocellulose conversion is essential for economically feasible production of bio-based chemicals and biofuels. One limiting step is highly selective processes to quickly decrystallize the compact cellulose structure for efficient hydrolysis. We evaluated the impact of trifluoroacetic acid (TFA) and phosphorous acid (PA)-induced swelling of crystalline cellulose on enhancement of enzymatic digestion. Results In this study, two swelling agents, TFA and PA, are compared and found to be highly efficient for cellulose decrystallization at low temperatures within 1 h. After treatment, the microfibril structure of swollen celluloses was observed to develop distinct microscopic morphology and subsequent enzymatic hydrolysis resulted over 90% cellulose conversion within 24 h. The crystalline cellulose change was determined by reduction of loss of X-ray diffractability, and loss of resistance to enzymatic hydrolysis. NMR results suggest that both TFA and PA efficiently converted most of the crystalline cellulose regions to amorphous regions through cellulose chain relocation that inhibits recrystallization. It was found that the swelling mechanism is different between TFA and PA. To the best of our knowledge, it is the first time to compare and quantify the cellulose regions transformation by swelling agents. Conclusion This study shows the low-temperature swelling of different celluloses in TFA and PA reduces recalcitrance of crystalline cellulose to enzymatic hydrolysis. TFA and PA are both ideal candidate swelling agents for a closed system for ease of solvent recovery by either simple distillation or filtration. This study provides potentially useful agents in large-scale deconstruction of biomass.

Background
Cellulose is the most abundant carbohydrate polymer on earth [1]. In recent decades, utilization of cellulose to produce biofuels, and functional molecules of interests have attracted numerous attention due to its renewable, carbon neutral, and environmental friendly features [1][2][3][4]. However, cellulose, a homopolymer of glucose, has a compact fibril structure possessing high crystallinity and extensive hydrogen bonds that are formed during biosynthesis which hinders the cellulose conversion efficiency [5,6]. Pretreatment is required to make the compact cellulose structure accessible to enzyme or chemical catalyst to efficiently hydrolyze β-1,4 glycosidic bond [7,8]. Unlike pretreatments such as dilute acid, ammonia fiber expansion, ammonia recycle percolation pretreatment demanding high temperature or pressure, and cellulose solvent-and organic solvent-based lignocellulose fractionation (COSLIF) can fractionate lignocellulose components at modest reaction conditions [9]. The previous study shows that concentrated phosphoric acid (PA) is an effective cellulose solvent [9]. PA is a modest acid; under mild condition, cellulose can completely dissolve into it when its concentration is more than a critical value [10]. The cellulose dissolving phenomenon is known as solvent swelling effect. Similar as PA, swelling agent trifluoroacetic acid (TFA) attracts attention as it can effectively

Open Access
Biotechnology for Biofuels swell cellulose and be recycled due to its low boiling point, which provides an efficient and economical way to lower the cellulosic crystallinity and break up hydrogen bonds within the crystalline region [10][11][12].
Even at low temperatures, swelling agents can efficiently diffuse into the interstices between the fibrillary structural units of cellulose, as well as penetrate both ends into the elementary crystallites. This causes drastic changes in the crystal lattice structure [13]. As a result of swelling, cellulose crystallinity is significantly decreased, and the number and size of micro pores are increased, generating increased solvent-accessible surface area for hydrolyzing catalysts, such as enzymes [14,15]. When swollen cellulose is further heated, gelatinization occurs which results in dissolution of the cellulose into the liquid solvent, which is a transparent liquid. Dissolution completely disrupts the cellulose crystalline supramolecular structure. Rapid precipitation of this dissolved cellulose results in a disordered, amorphous cellulose. In our previous study, TFA was used comparing the effect of adding a gelatinization and precipitation step to enhance enzymatic hydrolysis of cellulose to reducing sugars. The data reported there showed that adding gelatinization/ precipitation does not further improve the cellulose reactivity from both enzymatic and chemical hydrolysis. The only significant effect was the small portion of cellulose that was hydrolyzed during gelatinization which could not be easily recovered by precipitation [15]. Our previous study determined that swelling cellulose is the key step for improving the cellulose reactivity and indicated that most of the changes relevant to enhancing hydrolysis occur through the swelling process.
To further understand the effect of swelling agents on cellulose substructure, we use two distinct celluloses in nature-cotton linters (Sigmacell, particle size ca 50 μm) and microcrystalline cellulose (Avicel PH 101, particle size ca 50 μm) as substrates. The Avicel PH 101 cellulose was prepared using sulfuric acid to hydrolyze poplar which is a representative of woody biomass and leave with more crystalline region cellulose; while the Sigmacell was prepared using herbaceous cotton linters cellulose contains both crystalline and amorphous regions cellulose. The cellulose in both untreated cotton linters and microcrystalline cellulose are in the form of cellulose I [16]. We report, for the first time, on comparisons between the physicochemical characterization of two types of cellulose by two swelling agents, TFA and PA. Both of them are known as swelling reagents for high efficiency for cellulose decrystallization which are used at high concentration and low temperature (ca 0 °C) [10,11]. However, based on our observation, the celluloses after swelling by TFA or PA have different morphological appearances before ethanol precipitation. PA results in a transparent cellulose gel in a short time, while TFA as solvent has the unique advantage in recovery as it is readily volatile. The difference in cellulose morphology indicates that there might be differences in the swelling mechanisms. Even though both agents have been reported for cellulose conversion, no previous studies focused on study and comparison their swelling mechanism at low temperature.

TFA swelling of cellulose
One gram of cellulose was mixed with 30 mL of TFA (99%) in a 50 mL plastic Falcon tube. Then, the cellulose-TFA mixture was incubated at 0 °C for a predetermined period. After swelling, approximately 60 mL of ethanol (100%) was added with vigorous stirring, resulting in washed, swollen cellulose.
The cellulose was filtered through a Whatman GF/D filter paper and washed with an additional 30 mL ethanol (80%) at a rate of 10 mL per addition to remove residual TFA. Deionized water was used to suspend the cellulose pellet which was then freeze-dried (LAGCONCO, Kansas City, MO). The TFA-swelled cellulose was collected and named as S-TFA (Sigmacell TFA-swelled) or A-TFA (Avicel-TFA-swelled) for further analysis.

Phosphoric acid-swelled cellulose
The phosphoric acid-swelling process was based on Zhang's [17] method with the minor modifications described below. One gram of cellulose and 3 mL distilled water were mixed in a 50 mL plastic Falcon tube, and then, 10 mL 0 °C PA (86.2%) was added to the slurry with violent agitation. The resulting mixture was kept at 0 °C for a predetermined period with stirring every 10 min. The cellulose was precipitated with ethanol, filtered, washed with additional ethanol, and then freeze-dried by the same procedure as TFA swelling. The phosphoric acid cellulose was collected and named as S-PA (Sigmacell PA swelled) or A-PA (Avicel PA swelled) for further analysis.
All six samples, untreated cellulose S-U (Sigmacell untreated) and A-U (Avicel untreated), TFA-treated cellulose (S-TFA and A-TFA), and PA-treated cellulose (S-PA and A-PA), share a similarly and extremely fine white powder appearance after freeze drying.

Physicochemical and morphological characterization Scanning electron microscopy (SEM)
Scanning electron microscopy images were obtained via Hitachi S3400N (Japan) microscope at 15 kV accelerating voltage. Prior to SEM observation, samples were placed in an ion sputter coater and subjected to gold sputtering for 0.5 h. The surface morphology of cellulose samples randomly selected and captured at 500 magnifications.

Atomic-force microscopy (AFM)
Protocol of AFM experiment is previously reported by Chen et al. [18].

X-ray diffraction (XRD)
To determine the crystallinity of cellulose before and after TFA or PA treatment, samples were analyzed by X-ray diffraction (XRD). A LabX XRD-6000 (Shimadzu) diffract meter was used to generate pattern data with Cu Kα radiation source (λ = 1.54060 Å) over the angular range 2θ = 5-40° and a step degree of 0.04. The crystalline index (CrI) was determined using the empirical method [10]: where I 002 is the maximum intensity of 002 peak at approximately 2θ = 22°, while I am is the amorphous diffraction intensity at approximately 2θ = 18.0°.

Fourier transform infrared (FTIR) spectroscopy
The functional groups' change of swelled cellulose was analyzed by FTIR. The spectra were acquired on a Perkin Elmer Spotlight 400 FTIR spectrometer (Perkin Elmer, Waltham, MA, USA) working in a transmittance mode. Before scanning, the sample pellets were prepared by mixing with spectroscopic grade KBr in a ratio of 1:100 (w/w) and then pressed under vacuum. FTIR spectra were recorded at 4 cm −1 resolution and 64 scans per sample in the range of 4000-500 cm −1 .

Solid-state CP/MAS 13 C NMR
Solid-state 13 C CP/MAS NMR were performed at 100 MHz resonance frequency on a Bruker Avance DPX400 NMR spectrometer with a 4 mm CP/MAS probe and samples spinning at 5 kHz MAS speed. 13 C CP/MAS NMR were conducted with 3 ms contact time, 30 ms acquisition time, 2 s recycle delays, and in 1600 scans. The data were analyzed by MestReNova and Origin software.

Cellulose reducing-end determination
The molar concentration of reducing ends of the cellulose was measured by the modified BCA method [19]. BCA reaction solution A was prepared by dissolving 0.971 g of disodium 2,2′-bicinchoninate, 27.14 g of Na 2 CO 3 , and 12.1 g of NaHCO 3 dissolved in 500 mL of distilled water. Solution B contained 0.624 g of CuSO 4 ·5H 2 O and 0.631 g of l-serine dissolved in 500 mL of water. Equal volumes of solution A and solution B were mixed to form the working solution before use. One milliliter of BCA working solution was mixed with 1 mL sample solution containing cellulose (5 mg/mL) in a tube with stopper. Glucose solutions with concentrations from 0 to 50 µM were used as standards. The mixture was incubated for 30 min at 75 °C in a convection oven. For cellulose sample, the tubes were vortex-mixed every 10 min to increase the interaction between cellulose and working solution.
The tubes were rapidly cooled to 20 °C using tap water. After cooling, reaction mixtures were transferred to a centrifuge tube and centrifuged for 5 min at a speed of 10,000g. The supernatant was measured at 560 nm on a UV-Vis spectrometer.

Enzymatic hydrolysis
To evaluate the enzymatic digestibility of swelled cellulose, enzymatic hydrolysis experiments were performed in 50 mL plastic round-bottom Falcon tubes in triplicate in a thermostatically controlled shaker at 50 °C and 250 rpm. The initial solid loading of 1% (w/v) was added into 5 mL, 50 mM sodium citrate buffer where pH was maintained around 4.8. Cellic ™ Ctec2 (aggressive cellulase and a high level of β-glucosidase, Novozymes North America, Inc., Franklinton, NC), at an enzyme loading of 7.0 FPU/g cellulose, was used for treated and untreated samples. Sodium azide (0.2%, w/v) was added to inhibit microbial growth during hydrolysis. Samples were taken at predetermined intervals and immediately filtered through 0.2 μm nominal-pore-size nylon syringe filter (Pall, Port Washington, NY) to remove the residual substrate. Filtrate was then stored at − 20 °C until analysis by HPLC.
The glucose yield was calculated as: Glucose yield (% ) = concentration of glucose × volume of reaction total mass of cellulose × 100.
The concentration of glucose was quantified by HPLC using an HPX-87H AMINEX ion exchange column (Bio-RAD, Hercules, CA), a Waters 1525 pump, and a Waters 2412 Refractive Index detector (Waters, Milford, MA). The data were stored and processed using Empower ™ 2 Chromatography Data Software (Waters Corp., Milford, MA). The mobile phase was 5 mM sulfuric acid in distilled, deionized water at flow rate of 0.6 mL/min. The column temperature was maintained at 60 °C. The injection volume was of 50 µL. The concentrations of glucose in the aqueous phase were determined using external calibration standards.

Analysis of cellulose crystalline content by X-ray diffraction and FTIR spectroscopy
Crystallinity change as a function of increasing swelling time (0, 1, 12, 24, and 48 h) for both swelling agents was measured. For both PA and TFA, CrI decreased rapidly within 1 h at 0 °C and reached a stable CrI value (Table 1). For quantification purpose, 1 h was selected as swelling treatment time for fully swollen cellulose to compare the effect on cellulose substructural change. Taking into consideration of the economic feasibility and energy input for large-scale applications, temperatures lower than 0 °C were not considered as influencing factors in this study. However, temperatures of − 15 and − 20 °C (lower than the freezing point of TFA) were tested as a comparison with 0 °C in previously reported work [15]. The crystallinity indices (CrIs) reported that there were very close to our results obtained at 0 °C. Therefore, we used 0 °C as the standard swelling temperature for both TFA and PA in this work. The CrIs of the untreated Sigmacell and Avicel cellulose were 81 and 83%, respectively. TFA-treated Sigmacell and Avicel cellulose stabilized with CrI values of 42 and 38%, while PA-treated Sigmacell and Avicel were 25 and 29%, respectively. The trend of CrI changes suggests that PA disrupts cellulose crystallinity more efficiently than TFA.
By further analyzing the XRD spectra shown in Fig. 1, it further confirms that loss of crystallinity during swelling results in cellulose crystal transmutation. The untreated celluloses were in cellulose I polymorph patterns. From the spectra, both cellulose I α and cellulose I β coexist in the swelled samples. As shown with the designated peaks, 2θ = 16° represents the cellulose I α These data show that the cellulose I structure was disrupted and resulted in more disorder cellulose structure [24]. According to our previous study, cellulose II was formed by recrystallization of swollen cellulose when freeze-dried [15]. However, distinct spectra patterns between TFA-and PA-treated cellulose suggest that, besides recrystallization, there may be different effects on the crystal structure between the two swelling agents.
To further examine the changes to the cellulose structure, FTIR analyses of the samples were performed ( Fig. 2 and Table 2). Related to the crystallinity change before and after the swelling process, O-H stretching and C-H stretching were the two most significant signals that can be observed in the FTIR spectra.
The O-H stretching is observed at the absorbance of 3600-3100 cm −1 which includes valence vibration of hydrogen-bonded OH groups, i.e., the intramolecular hydrogen bond, intermolecular hydrogen bond, and the O-H stretching. The signal intensities are significantly weakened by PA swelling, while TFA swelling does not result in as significant change as PA does. The O-H stretching corresponds both to the larger CrI decrease by PA than by TFA and to the XRD spectra results. Both TFA and PA can significantly decrease the cellulose structure crystallinity, while decrystallization by PA was more complete than by TFA.
The C-H stretching signal indicates a high percentage of long-chain hydrocarbons in the sample. Decreasing signal for C-H stretching (2895 cm −1 ) was observed after the swelling process in both substrates. PA decreased more than TFA. This is consistent with what was observed in the XRD results.
The drastically decrease of crystallinity of cellulose and lignocellulosic substrates to more amorphous cellulose regions was also observed by treating cellulose residue using ionic liquids, confirming that both swelling agents and ionic liquids could enhance the cellulose conversion efficiency by breaking the cellulose microstructure hydrogen bond to increase the accessible area [24].

Study redistribution and recrystallization of cellulose content by CP/MAS 13 C NMR
In addition to XRD and FTIR, CP/MAS 13 C NMR provided insight into cellulosic substructural changes from swelling treatment. Larsson et al. [25] and Wickholm et al. [26] determined spectral fitting methods and assignment for cellulose using cotton linter and several other cellulose sources. They found that the deconvoluted NMR signal of C-4 region (80-92 ppm) gives key information on the specific cellulose fibril structure. The C-4 region divided into two distinct region ranges: crystalline region and amorphous region [27]. Region from 86 to 92 ppm represents cellulose in more ordered forms. It is crystalline and defined separately as I α at 89.35 ppm, I (α+β) at 88.5 ppm, and I β at 87.91 ppm. A fourth form of cellulose with more mobility was determined and named as paracrystalline at 88.25 ppm. It was in-core or beneath fibril with less-ordered crystalline form compared to the others. All of the four crystalline regions are surface inaccessible to the surrounding solvent.
Region from 80 to 86 ppm represents more disordered forms or amorphous cellulose. They are defined as (solvent) accessible fibril surface, (solvent) inaccessible fibril surface and cellulose oligomers, respectively [26]. Solvent-accessible fibril surface at 84.95 and 83.26 ppm is determined to be amorphous cellulose at fibril or crystallite surfaces accessible to surrounding solvent [28,29]. In contrast, inaccessible fibril surface at 83.43 ppm are defined as amorphous form cellulose portion which is inaccessible to the surrounding solvent. Cellulose oligomers at 81.68 ppm are defined as less-ordered forms of carbohydrates or hemicellulose residues [25]. From Fig. 3 and Table 3, the NMR spectra showed that untreated celluloses had a dominant crystalline peak compared to the amorphous region. After TFA swelling process, the amorphous region was significantly enhanced, while the crystalline region decreased. The peaks change trends correspond to the CrI decrease after TFA swelling treatment. After PA treatment, more severe decrease of the crystalline region could be observed. The dominant peak fell on amorphous region. With the transition of the crystalline peaks to the amorphous peaks, the CrI value of both PA-treated sample decreased more substantial than the TFA-treated sample ( Table 1). The CrI value changes indicate the H-bonding rearrangement undergoing within the cellulose structure for both TFA-treated and PA-treated sample. The PA treatment has more thoroughly change in the cellulose substructure  than TFA swelling treatment, which can be further confirmed and analyzed by the deconvoluted peaks. Deconvoluted peaks (green line) show the details of assignment and spectra fitting of CP-MAS NMR signal in the C-4 region before and after swelling (Table 3 and Fig. 3). As the crystalline and amorphous region signals are complementary, the deconvoluted peak signals are integrated with total intensity of 100%. Figure 4 compares relative signal intensities using the deconvoluted peak results of Fig. 3. Untreated cotton linter has a large percentage which includes crystalline and paracrystalline portion. For the amorphous portion, the inaccessible fibril surfaces are larger than accessible fibril surfaces. After TFA treatment, the accessible fibril surface was dramatically increased by 50.6% with the inaccessible fibril surface portion and crystalline and paracrystalline  Table 3 regions decreased, indicating the disruption of rigidly crystal cellulose construction into amorphous one. PA treatment further increased the accessible region while maintaining similar inaccessible fibril surface fraction. However, accessible fibril surfaces increase to 87.2%. Most of the crystalline and paracrystalline regions declined to approximately 2% in total. It is worth noting that the relative intensities among crystalline region I α , I (α+β) , and I β was not significantly changed. This is likely due to the low temperature of the swelling treatment, as solid-state conversion of I α to I β requires high temperature for transforming the crystals [30]. Interestingly, when Avicel is used as cellulose substrate, a similar transition is observed.
Overall, the inner structure of cellulose was disrupted after being treated by TFA, whereas PA treatment can further improve accessible fibril surfaces region into dominant percentage.

Analysis of crystalline cellulose change by microscopy
Scanning electron microscopy was used to evaluate changes in surface morphology of cellulose substrate before and after swelling. Figure 5 shows the SEM images at 500× magnification. Untreated cellulose particles are generally rod-shaped and with orderly arranged fibers observable on the cellulose surface.
After swelling with TFA, a significant morphology change was observed compared to untreated cellulose substrates. The surface of the particles was more irregular with some fibers visible protruding from the surface. This confirms the previous results which showed that the TFA dimer at low temperature can penetrate the cellulose structure to swell the cellulose [11].
The phosphoric acid treatment did not generate the same morphological surface change as TFA treatment. The particles were distributed loosely after PA swelling, while the surface of the particles was similar to the untreated cellulose. This is in contrast to the XRD and FTIR data which show greater changes during PA treatment.
Different swelling mechanisms between TFA and PA were also observed by atomic-force microscopy (AMF). Figure 6 shows the AMF images of the native and swollen cellulose substrates. Untreated cellulose has longitudinal and rod-shaped particles. Untreated microcrystalline cellulose (Avicel) has a larger diameter compared to cotton linters. After the TFA treatment, the length of microcrystalline cellulose was dramatically reduced to shorter fractions and loosely distributed with a more spherical shape due to TFA swelling. Sigmacell cotton linters with native amorphous regions provide unique advantage for the TFA molecules penetration and generate large sphere interconnected swollen cellulose. The length of the TFAtreated cotton linters does not change significantly.
The PA-swollen Avicel and Sigmacell celluloses were observed with cellulose redistribution and recrystallization with thinner-shaped rod. Celluloses are more tightly packed compared to loosely distributed TFA-treated celluloses. The AFM images also show that swollen effect of PA is not as obvious as TFA, in contrast to the XRD and FTIR data which show the opposite.

Rates of enzymatic hydrolysis of swollen cellulose
The analyses described above show significant decreases in cellulose crystallinity and increases in solvent-accessible cellulose surface, both of which correlate strongly with cellulose digestibility by enzymes [31,32]. We have confirmed this by performing enzymatic hydrolysis of the untreated, TFA-treated, and PA-treated cellulose samples (Fig. 7). Prior to swelling, the saccharification rates and yields of both varieties of cellulose are low (ca 40%) through 24 h of reaction. The results are consistent with the previous reports and indicate that cellulose is partially accessible due to their compact crystalline structure [10]. The untreated Sigmacell yields 6% more glucose  compared to Avicel, which might be due to the amorphous regions of cotton linters compared to high crystallinity of Avicel. After treatment by PA or TFA, the enzymatic hydrolysis yields were dramatically improved for both varieties of cellulose, likely due to the reduced crystallinity. PA-treated cellulose has a higher initial kinetic rate (0-3 h) compared to TFA-treated cellulose. This corresponds to the higher percentage of solvent-accessible fibril surfaces in PA-treated celluloses compared to TFA-treated celluloses. Interestingly, PA-swelled Sigmacell has a higher glucose yield compared to PAswelled Avicel, while maintaining a similar initial hydrolysis rate. This can be interpreted that, even after swelling, the microcrystalline Avicel that is manufactured by sulfuric acid hydrolysis of wood pulp retains some of its recalcitrance after PA swelling. Compared to PA-treated cellulose, TFA-treated Avicel and Sigmacell do not have a significant difference both on trend of glucose yield and reaction rates. Both of the TFA-treated celluloses were fully converted into glucose by 24 h. However, the slower rates during the initial hydrolysis for TFA-treated cellulose correlated to the lower fraction of fibril that was solvent accessible compared to PA-treated celluloses. As the surface of the TFA-treated cellulose was hydrolyzed, the newly exposed paracrystalline and cellulose oligomers were quickly digested.

Analyses of swelling agent effect on celluloses
Low-temperature swelling by TFA and PA results in efficient disruption of cellulose crystallinity. TFA is recommended as it has a wide operational condition (− 20 to 78 °C) and distillation will be feasible after the swelling process. Compared to TFA, PA is unique that during swelling process, cellulose becomes a gel in a short time. PA may be also recyclable after the swelling process.
X-ray diffraction results showed that, after swelling, CrI indices were decreased significantly for both swelling agents. PA has a larger decrease in CrI. More importantly, XRD spectra shifts showed that PA and TFA may have different swelling mechanisms. The SEM and AFM results showed different morphological changes after the TFA (flaky on the surface of the swelled cellulose), while PA treatment does not generate as significant change as the surface of TFA-swelled cellulose. Furthermore, AFM showed that TFA treatment results in predominantly spherical particles, while PA generated cylindrical morphology. The FTIR results confirmed that the intramolecular bonding was changed during treatment. The different effects and extent of cellulose microstructure changes are determined and analyzed by deconvolution of NMR data. The results clearly showed that choice of swelling agent is more important than cellulose source for determining the type and extent of changes to the cellulose.
Enzymatic hydrolysis is the most effective way to evaluate the resulting change in cellulose reactivity after swelling. We found that the early stage (< 6 h) hydrolysis of phosphoric acid-swelled cellulose has significantly higher initial hydrolysis rates compared to TFA. Regardless of cellulose type, phosphoric acid can convert more of the cellulose into accessible fibril surface, with 29% higher accessible surface than TFA. This might explain why the phosphoric acid treated cellulose has faster initial hydrolysis rates than TFA-treated cellulose. Combined with the results of sugar reducing ends after swelling process, as shown in Table 4, TFA has a more significant effect on exposing increased sugar reducing ends at low temperature, while PA mainly physically relocates the cellulose from largely solvent inaccessible to more solvent accessible. This might be explained by the fact that concentrated PA has a freezing point slightly below 4 °C. TFA has less effect on changing cellulose crystal forms, but has a higher impact on decreasing the degree of polymerization of cellulose.
Taken together, these data give us insights into different mechanisms for TFA and phosphoric acid-swelling process. CrI change of cellulose is not the only indicator on cellulose reactivity, and more importantly, cellulose substructure change should be taken into consideration, since swelling agents function differently.
Both TFA and PA treatments resulted in a significant cellulose decrystallization. We used CP/MAS 13 C NMR as a non-destructive tool to further quantify changes to the cellulose super-molecular structure upon TFA and PA treatment [26]. We found the spectral fitting of NMR spectrum of cellulose I signal cluster between 80 and 92 ppm which have significant different strength between the TFA-and PA-treated cotton linters and microcrystalline cellulose [26]. The NMR results confirmed higher solvent-accessible fiber region of PA treatment cellulose correlate to higher initial hydrolysis  rates for cellulose by enzymes. TFA caused significant changes to the microscopic morphology of the cellulose character compared to PA-treated cellulose, TFAtreated cellulose presented a lower percent of the total portion that was solvent accessible but has a much higher amount of reducing sugar ends, suggesting partial hydrolysis of the cellulose during treatment.
To sum up, swelling mechanisms between PA and TFA lie in three ways. First, morphologically, PA generates spherical swollen cellulose, TFA generates cylindric swollen cellulose. The morphology of the swollen celluloses indicates that PA results in a more thoroughly swelling change in cellulosic substructure as more space was created in the sample (confirmed by XRD results). Second, chemically, the PA is more efficient in creating inner space by breaking inter cellulose chain hydrogen bonds than hydrolyzing 1,4-glycosidic bonds in cellulose chain, while TFA functions as dimer to create more porous cellulosic structure under low temperature, as well as generated much higher sugar reducing ends content than PA. Third, effects on enzymatic hydrolysis conversion efficiency of two swelling agents demonstrated that creating cellulose inner space between cellulose chain is more important than creating sugar reducing ends for anchoring biocatalyst active site.

Conclusions
The results demonstrated that TFA and PA have different mechanisms on cellulose modification, but result in similar yields during enzymatic hydrolysis after 24 h. Hydrolysis data showed that cellulose after TFA treatment has more homogeneity as TFA catalyzed and exposed cellulose with sugar reducing ends. PA treatment has more impact on cellulose internal fibril structure at low temperature, but does result in more cellulose chain ends as determined by exposed reducing sugars. The swelling process demonstrated that crystallinity change is not the only indicator for cellulose modification and reactivity improvement, while cellulose substructure change is more important.


extfieldsofstudy:
Materials Science
Medicine

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Advantages and Limitations of Current Imaging Techniques for Characterizing Liposome Morphology.

There are currently a number of imaging techniques available for evaluating the morphology of liposomes and other nanoparticles, with each having its own advantages and disadvantages that should be considered when interpreting data. Controlling and validating the morphology of nanoparticles is of key importance for the effective clinical translation of liposomal formulations. There are a number of physical characteristics of liposomes that determine their in vivo behavior, including size, surface characteristics, lamellarity, and homogeneity. Despite the great importance of the morphology of nanoparticles, it is generally not well-characterized and is difficult to control. Appropriate imaging techniques provide important details regarding the morphological characteristics of nanoparticles, and should be used in conjunction with other methods to assess physicochemical parameters. In this review, we will discuss the advantages and limitations of available imaging techniques used to evaluate liposomal formulations.

INTRODUCTION
Liposomes are a type of nanocarrier that have been widely investigated for drug-delivery purposes. They are composed of phospholipid bilayers which enclose a distinct aqueous space, thereby allowing encapsulation of both hydrophilic and hydrophobic compounds (Metselaar and Storm, 2005). Liposomes are able to stabilize therapeutic compounds and overcome barriers to cellular and tissue uptake (Ding et al., 2006;Hua and Wu, 2013). This allows them to improve targeting of compounds to sites of disease and consequently reduce accumulation in non-target organs (Bakker-Woudenberg et al., 1994;Mastrobattista et al., 1999;Hua, 2013;Hua et al., 2015;Sercombe et al., 2015;Zununi Vahed et al., 2017). There are four main types of liposomes based on their surface characteristics -conventional liposomes, PEGylated liposomes, ligand-targeted liposomes, and theranostic liposomes (Figure 1; Sercombe et al., 2015). Ligand-targeted liposomes provide the potential for site-specific delivery of drugs to certain tissues or organs that selectively express the targeted ligand (Willis and Forssen, 1998;Bendas, 2001;Sawant and Torchilin, 2012), whereas PEGylated liposomes confer steric hindrance to enhance the circulation half-life of the delivery system following systemic administration (Torchilin, 1994;Wang et al., 2015). Liposomes incorporating a combination of the various delivery platforms can further improve the delivery of encapsulated compounds, depending on the route of administration and site of disease.
Following the manufacturing process, liposomes are characterized to ensure homogeneity across a number of parameters, including drug encapsulation, ligand-conjugation, lipid composition, surface charge, and morphological properties (e.g., size, shape, and number of lamellae) (Kuntsche et al., 2011). These characteristics are important as they can have a major impact on the behavior of liposomes both in vitro and in vivo (Sawant and Torchilin, 2012;Sercombe et al., 2015). Recognition and clearance of liposomes by the body's defenses, including the reticuloendothelial system (RES) and adsorption of opsonins with subsequent uptake by the mononuclear phagocytic system, are major contributors to the clearance and degradation of liposomes (Senior, 1987;Cullis et al., 1998;Ishida et al., 2001). Therefore, being able to determine the physicochemical properties of manufactured liposomes is important to optimize a formulation for further translational evaluation.
A major aspect in the physicochemical assessment of liposomes is visualizing the morphology of the nanoparticles using microscopy. There are a number of techniques available for imaging liposomes and other nanoparticles that can be broadly categorized into light, electron, or atomic-force microscopy (Bibi et al., 2011). Each technique has its own advantages and limitations, which should be considered when evaluating studies on nanoparticle-based drug delivery systems (Table 1). This review will evaluate each imaging technique used to assess the morphological characteristics of liposomes.

LIGHT MICROSCOPY
Light or optical microscopy refers to microscopes that utilize visible light and an arrangement of lenses to magnify a field of view (Murphy and Davidson, 2012c). Basic light microscopy itself is unable to provide comprehensive information about the lipid bilayer compared to the detail offered by other microscopy techniques. However, it can be used to rapidly obtain an image of vesicles using basic laboratory equipment (Bibi et al., 2011). This technique can be particularly useful when gathering general information on the size, shape, homogeneity, and degree of aggregation of a liposome sample (Nallamothu et al., 2006). Light microscopes have an ultimate resolution of ∼250 nm (governed by the smallest diffraction-limited spot size that can be achieved by the instrument) and, as such, are typically incapable of providing detailed information regarding the structures of small unilamellar vesicles (SUVs) and the lamellarity of vesicles (Bibi et al., 2011). Generally, light microscopy can only provide significant information on giant unilamellar vesicles (GUVs), which can range from single to hundreds of micrometers in diameter (Bagatolli, 2009). Incorporation of fluorescent probes, polarization techniques, and application of high-resolution confocal microscopy can provide more information about the 3D structure and lamellarity of the vesicles (Bagatolli, 2009).

Polarization Microscopy
This type of light microscopy utilizes the unique ability of polarized light to interact with polarizable bonds of ordered molecules (Murphy and Davidson, 2012d). Enhanced light absorption occurs when molecular dipoles in the sample are aligned with the polarization vector of the incident light wave; resulting in phase differences between sampling light rays, which in turn produce interference-dependent changes in amplitude in the image plane (Murphy and Davidson, 2012d). Image contrast then arises not only from the effects of interference and diffraction, but also due to the presence of ordered molecular arrangements (Murphy and Davidson, 2012d). It can be used to study the form and dynamics of many ordered cellular structures, including lipid bilayers of plasma membranes (Bibi et al., 2011;Murphy and Davidson, 2012d). Images can be obtained in either monochrome or color. This technique provides an alternative method to visualize liposomes, particularly to confirm the formation of vesicles. It does not provide conclusive observations regarding the bilayer characteristics or lamellarity of the vesicles (Bibi et al., 2011). In addition, polarization microscopy is also limited by the size of the vesicles that can be visualized, with large vesicles in the micrometer range having the optimal clarity. This technique does not provide clear images of particles in the nanometer range.

Fluorescence Microscopy
Fluorescence or "epifluorescence" microscopy is a special form of light microscopy that exploits the ability of fluorochromes to emit light after being excited with light of a certain wavelength (Murphy and Davidson, 2012b). This technique is widely used in biophysics to provide essential information about the structure and dynamics of membrane components (Bouvrais et al., 2010). In these studies, fluorescent probes are specifically incorporated into the membrane under investigation, permitting visualization of the structure. A large range of fluorescent dyes with various chemical and photonic properties are commercially available, catering to a variety of research questions. For example, certain fluorescent dyes may prefer specific membrane environments exhibiting different arrangements and lateral packing (Bouvrais et al., 2010). As such, fluorescence microscopy allows for the potential to simultaneously apply multiple probes within a sample to provide information about the membrane structure itself. The fluorescent probes can be placed within the aqueous compartment as well as the lipid bilayer of liposomes (Bibi et al., 2011). This arrangement can be especially useful when viewing GUVs, where information can be obtained regarding the shape, size and fluidity of the lipid vesicles (Klymchenko et al., 2009;Bouvrais et al., 2010). Incorporation of probes, such as rhodaminelabeled lipids, directly into the lipid bilayer can also allow visualization of the lamellarity of liposomes (Bibi et al., 2011).
The disadvantage of fluorescent microscopy is that the addition of probes and dyes to a membrane system can potentially interfere with the properties of the liposomal delivery system (Bouvrais et al., 2010;Bibi et al., 2011;Murphy and Davidson, 2012b). However, the use of low dye concentrations (≤1 mol%) has been shown to have minimal impact on the physical properties of the membrane (Bouvrais et al., 2010). It is also important to note that the choice of fluorescent dye is a critical step, as some dyes can induce large changes in the host membrane and/or cause experimental artifacts, resulting in inaccurate data interpretation (Bouvrais et al., 2010). In addition, photo-induced lipid peroxidation can also lead to domain formation even in simple dye systems. This process can result in the formation of large rafts and spontaneous facet formation (Bouvrais et al., 2010). Prolonged exposure to fluorescent light can also result in bleaching and loss of fluorescence intensity. Therefore, the choice of fluorescent dye and the development of new membrane probes are important considerations when using fluorescence microscopy (Klymchenko et al., 2009).

Confocal Microscopy
Confocal scanning microscopy has been an advancement in the area of fluorescence microscopy. Rather than illuminating the entire sample, an image is built by scanning one (or more) focused beams of light across the sample. Light returning from the illuminated sample passes through an aperture that rejects out-of-focus light from above and below the plane of interest; ensuring that only images from a small depth of field are obtained, greatly improving the out-of-plane resolution (Bibi et al., 2011;Murphy and Davidson, 2012a). Using this technique, a "z-stack" of images is collected, starting from the top of the vesicle followed by images taken in defined z-increments to the bottom of the sample, resulting in a composite 3D image of the sample (Bibi et al., 2011;Murphy and Davidson, 2012a). Confocal scanning microscopy has become a more attractive technique over epifluorescent light microscopes due to its superior image clarity. In the case of GUVs, this technique is capable of visualizing the internal structure of the lipid systems, which is often not possible with other microscopy methods (Ruozi et al., 2011;Mertins and Dimova, 2013). For example, separation of the aqueous and lipid bilayer phase can be clearly visualized in larger vesicles (Mertins and Dimova, 2013). However, confocal microscopy is still diffraction-limited and, therefore, unable to produce high-definition images of SUVs or oligolamellar liposomes (Ruozi et al., 2011).

ELECTRON MICROSCOPY TECHNIQUES
Electron microscopy is a method for the visualization of vesicles under very high magnification (Henry, 2005). It is widely used in the characterization of lipid vesicles as the electron wavelength (and hence diffraction-limited resolution), is many orders of magnitude lower than that of optical microscopy, and therefore provides super-resolution for clear visualization of small liposomes (Bibi et al., 2011;Ruozi et al., 2011). This technique uses a beam of electrons focused onto the surface  • Can provide information about the mechanical and chemical properties of a sample surface through force measurements • Requires nanoparticles to be adsorbed onto support surfaces, which can modify the size and shape of the vesicles • Periodic contact of the probing tip can drag the liposomes as it moves across the vesicles in a sample of the sample by various electromagnetic lenses. The electrons are then scattered by the sample, and are then refocused and magnified by a further series of electromagnetic lenses in the imaging column to produce a projected image (Henry, 2005). There are a number of different types of electron microscopes, each requiring a different sample preparation method.

Scanning Electron Microscopy (SEM)
Scanning electron microscopes (SEMs) utilize an electron beam that is scanned across or over a sample (rather than through a sample) to produce a magnified image of an object (Adler and Schiemann, 1985). Alder et al. first attempted to use SEM to characterize liposomes in 1984 (Adler and Schiemann, 1985). They showed that using the freeze-drying method to prepare the liposome samples for SEM resulted in a large proportion of visible lumps and crusted material (Adler and Schiemann, 1985). More recent studies have shown that the liposome structure itself may suffer perturbations due to the high-vacuum conditions and staining processes required for this preparation technique (Ruozi et al., 2011). SEM is now not commonly used for analyzing liposomes because it requires the sample to be dried or fixed prior to imaging (Ruozi et al., 2011). However, SEM can provide general information on the concentric structure of the different lipid layers, as well as give detail on the size and spherical morphology of a preparation (Nirale et al., 2009).

Transmission Electron Microscopy (TEM)
Transmission electron microscopy (TEM) is the most frequently used imaging method for the evaluation of the structure of nanoparticles (Henry, 2005;Kuntsche et al., 2011). It involves the preparation of a thin sample (<100 nm thick) that is placed in a vacuum chamber. The electron beam crosses the sample, where it is then focused by the objective lens to form an image. TEM can readily image soft-matter samples with a spatial resolution down to well below 1 nm in size (Henry, 2005;Kuntsche et al., 2011;Ruozi et al., 2011). This technique can be further categorized based on the sample-preparation method utilizedin particular, negative staining, freeze-fracture and cryogenic TEM. It can provide information on surface modifications of nanoparticles as it provides better contrast and contour of images than other microscopy techniques. For example, conjugation of transferrin to the surface of DSPC/cholesterol liposomes showed a particulate surface coating with negative-stain TEM, which was absent in the unconjugated liposome preparations (Anabousi et al., 2005). In a separate experiment, specific antibodies raised against human transferrin were added before the negative stain procedure to confirm the identity of the particulate surface coating (Anabousi et al., 2005). Therefore, TEM provides the possibility to achieve much-improved resolution when assessing the conjugation of ligands to the surface of liposomes.

Negative Staining Technique
In negative-stain TEM, sample preparation involves a small amount of hydrated sample being placed onto a grid. As the vesicles are in direct contact with the grid, their orientation and morphology may be affected, and hence this should be taken into account (Bibi et al., 2011). It is also necessary to embed the liposomes in a suitable electron-dense material (e.g., heavy metal salts like uranyl acetate or phosphotungstic acid) that provides high contrast, so vesicles can be viewed against a dark-stained background (Ruozi et al., 2011). The negativestaining technique is relatively fast and simple; however, it has been shown to cause changes to the original liposome structure and can lead to the creation of light and dark fringes that may be mistaken for lamellar structures (Bibi et al., 2011;Ruozi et al., 2011). In addition, placing the sample under vacuum can cause further dehydration of the sample, which can again cause changes in the structure of the vesicles (Bibi et al., 2011). Whilst the negative-stain TEM technique can provide much higher magnification for imaging nanoparticles, the damage to the liposome structure makes it difficult to accurately evaluate the morphological characteristics of the sample. Therefore, other imaging techniques are generally required to confirm results.

Freeze-Fracture Technique
The freeze-fracture technique does not require any drying process and can provide additional information about the internal structure of nanoparticles (Kuntsche et al., 2011). This method involves placing a sample on a TEM grid that is sandwiched between two copper or gold holders (Severs, 2007). The sample is vitrified via rapid freezing, typically with liquid propane or liquid nitrogen, before being fractured along areas of the sample with weak molecular interactions. This fracture surface can be further etched and shadowed with a thin platinum or carbon layer to provide a "negative" replica of the fracture sample plane (Severs, 2007;Kuntsche et al., 2011). The replica is then cleaned with an organic solvent to remove all residues prior to visualization under a TEM microscope. As these replicas are so stable, they can be stored and viewed later (Kuntsche et al., 2011). The major advantage of this technique is that the replicas closely reflect the native state of the sample, and can provide detailed information on the 3D structure of the vesicles and bilayer organization (Bibi et al., 2011;Kuntsche et al., 2011). This information includes the multilamellar construction and bilayer packing of multilamellar vesicles. This technique can also assess aggregate size and may be particularly useful for examining the interaction of cationic liposomes with DNA (Bibi et al., 2011). However, artifacts may still occur in the sample during preparation due to insufficient freezing rate, re-deposition of solvent molecules and/or mechanical stress (Severs, 2007;Kuntsche et al., 2011). For example, a "rippling effect" can occur on the SUVs in the sample (Bibi et al., 2011). This is a common bilayer deformation that is due to a disorder in the transitions of the acyl chains prior to freezing. Incubating vesicles between the pre-transition and actual transition temperature can also cause ripples (Bibi et al., 2011). Interestingly, this can be used to provide information into the lipid phase transitions that occur with the varying nature of lipids used for liposomal preparations (Bibi et al., 2011).

Cryogenic TEM
Cryogenic TEM (cryo-TEM) is a variation of TEM where thin aqueous hydrated films which are vitrified in liquid ethane are used prior to imaging. This technique allows for the analysis of liposomes in their most native state, and is a valuable tool to determine the size, shape, internal structure, and lamellarity of liposomes (Weisman et al., 2004;Bibi et al., 2011;Kuntsche et al., 2011). The major advantage of rapidly freezing liposome samples is minimizing the formation of ice crystals and preserving proteins or other materials (Bibi et al., 2011;Kuntsche et al., 2011). This is beneficial where proteins or DNA have been encapsulated within the nanoparticles (Weisman et al., 2004;Kuntsche et al., 2011). Cryo-TEM is the most useful form of microscopy currently available to study liposomes, as it avoids issues with chemical fixation, dehydration, cutting and stainingall of which can affect the morphology of vesicles (Bibi et al., 2011). The resolution range is ∼5 to 500 nm, as defined by the thickness of the film (Almgren et al., 2000). However, several limitations should be noted with this technique, including the fact that only a 2D image is obtained from 3D objects (which generally also applies to most of the other microscopy techniques). To overcome this limitation, reconstruction of 3D shapes from a sufficiently large number of 2D images of randomly oriented non-spherical particles is possible (Orlova et al., 1999), as well as viewing the sample at different tilt angles to attain information about 3D shape (Van Antwerpen and Gilkey, 1994). Cryogenic electron tomography (cryo-ET) can also be used instead to attain 3D images (Le Bihan et al., 2009). Cryo-TEM also utilizes lower doses of electrons, which means that it often has a lower resolution compared to other methods (Bibi et al., 2011;Kuntsche et al., 2011). Artifacts are still possible due to the formation of a thin film of amorphous ice and the use of blotting on the sample applying shear forces during the film formation (Almgren et al., 2000;Bibi et al., 2011).

Environmental Scanning Electron Microscopy (ESEM)
Environmental scanning electron microscopy (ESEM) is an imaging system that does not require the use of fixing, staining or freezing of vesicles, and can allow imaging of dynamic changes of wet systems without previous sample preparation (Muscariello et al., 2005;Ruozi et al., 2011). The main feature of ESEM is the presence of water vapor in the microscope chamber. The ability to maintain a water-containing atmosphere around the sample that may be partially or even fully hydrated is made possible by the use of a multiple-aperture, graduated vacuum system that allows the imaging chamber to be sustained at pressures up to 55 hPa (Bibi et al., 2011;Ruozi et al., 2011). The primary electron beam can generate secondary electrons that then encounter vapor molecules, leading to a cascade amplification of the signal before reaching the detector. Because of this, ESEM does not require sample preparation (Muscariello et al., 2005). This technique allows for variation in the sample environment through a series of pressure, temperature and gas compositions (Mohammed et al., 2004), which is useful when determining how environmental changes affect the vesicles. This is applicable to nanopharmaceutical formulation and stability studies (Bibi et al., 2011). ESEM has also been used to analyze drug loading into the bilayer of liposomes (Mohammed et al., 2004), as well as determining the size and shape of vesicles. A limitation of ESEM is that it cannot provide detailed information regarding the lamellarity and internal architecture of the nanoscale structures (Ruozi et al., 2011).

ATOMIC FORCE MICROSCOPY (AFM)
Atomic force microscopy (AFM), also known as scanning-force microscopy (SFM), is a type of scanning probe microscope technique. It works by running a sharp tip attached to a cantilever and sensor over the surface of a sample and measuring the surface forces between the probe and the sample (Sitterberg et al., 2010). As the cantilever runs along the sample surface, it moves up and down due to the surface features and the cantilever deflects accordingly. This deflection is usually quantified using an optical sensor, with the laser beam being reflected on the back of the cantilever onto the light detector (Sitterberg et al., 2010). AFM does not need to operate in a vacuum and can operate in ambient air or under liquid; hence it is increasingly being used to image biological samples as well as nanoparticles (Liang et al., 2004a,b;Ruozi et al., 2005Ruozi et al., , 2009). AFM has outstanding resolution in the order of fractions of a nanometer and can provide a 3D image of liposomes along with details on morphology, size distribution, homogeneity, and stability (Liang et al., 2004a,b;Ruozi et al., 2005Ruozi et al., , 2007Ruozi et al., , 2009. Importantly, AFM can be used to characterize the surface modifications of liposomes and detect ligands (e.g., antibodies and polymers) conjugated at the liposomal membrane surface (Bendas et al., 1999;Moutardier et al., 2003;Anabousi et al., 2005;Liang et al., 2005). For example, Bendas et al. used AFM to magnify the liposomal membrane border and were able to image trimeric structures, approximately 8-10 nm in diameter, which represented the coupling of IgG antibodies to the liposome surface (Bendas et al., 1999). The findings showed that the effectiveness of the technique was highly dependent on the conjugation method used, with antibodies conjugated directly to the liposomal surface being visible with AFM (restricted protein mobility), in comparison to antibodies attached to PEG chains. It was suggestive that the PEG chains caused high protein mobility and, therefore, were unable to be scanned (Bendas et al., 1999). Conversely, Anabousi et al showed that incorporation of PEGylated lipids into the liposomes induced a steric stabilization with liposomes maintaining a spherical shape (Anabousi et al., 2005). AFM images of the surface of unconjugated liposomes were smooth and no structures could be observed, whereas conjugation of transferrin to the surface of PEGylated liposomes were visualized as small globular structures (Anabousi et al., 2005). Similarly, Moutardier et al manufactured liposomes with polymeric cores (LSP) that consisted of drugs loaded into polymeric particles that formed the core of lipid vesicles (Moutardier et al., 2003). Images taken using AFM showed the presence of a polymer network on the exterior surface, which suggested that the collagen polymeric core radiated out and formed a surface layer on the LSP (Moutardier et al., 2003). In addition to surface structural details, AFM can provide information about the mechanical and chemical properties of a sample surface through force measurements (Ruozi et al., 2007). For example, Mao et al. used this technique to assess the elasticity and adhesive properties of liposomes (Mao et al., 2004). One limitation of AFM is the need for nanoparticles to be adsorbed onto support surfaces, such as mica or silicon wafers. The adsorption of liposomes onto a solid substrate has the potential to modify the size and shape of the vesicles, and cause their flattening. (Ruozi et al., 2007). In addition, the periodic contact of the probing tip can drag the liposomes as it moves across the vesicles in a sample (Jass et al., 2000). Despite this, AFM is still a useful tool in the evaluation of liposomes.

CONCLUSION
There is a wide range of imaging techniques available for evaluating the morphology of liposomes, with each having its own advantages and disadvantages. Light microscopy can provide general details regarding the size and shape of larger vesicles and the homogeneity of a sample in a relatively fast manner. Conversely, TEM is the most commonly used technique to examine the morphology of liposomes in much more detail; however, the potential for structural changes with each TEM sub-type due to staining and/or exposure to vacuum conditions need to be considered when interpreting the results. ESEM is most useful when determining liposomal changes in response to the environment, whereas AFM is emerging as a useful method in the morphological analysis of nanoparticles and provides maximum resolution of the liposomal surface. Both AFM and TEM are capable of imaging ligands conjugated to the surface of liposomes and provide complementary information on surface modifications. Overall, the choice of technique is dependent on what morphological characteristics and degree of detail are required. In addition, understanding the potential effects of the sample preparation method of each imaging technique is important in the selection process.

AUTHOR CONTRIBUTIONS
Drafting of manuscript: A-LR and SH. Revising the article critically for important intellectual content: SH, PD, JF, WP, AM, DS and AW.