Summary

It’s helpful to have rich mental models of the structures of the brain; otherwise, it is difficult to integrate empirical data when reasoning about what happens to the brain during the dying and preservation processes. Based on my research, I currently feel that the most important set of structures in the brain are water-soluble gel-like networks of biomolecules that are in a constant state of remodeling. We can study the properties of these gel-like structures: how strong they are, how they might break down or be strengthened, how other biomolecules are anchored to them, and how they can be measured. It seems to be better when structures in the brain that contain the information content for valued cognitive processes, like engrams, are integrated into or anchored in stronger gel-like networks, because stronger gel-like networks seem to be relatively better maintained during the dying and preservation processes.

The structures of the brain during everyday life

Like other organs, the brain is obviously not a bag of liquid. And neither are the individual cells inside of the brain. And yet the brain and its constituent cells are clearly not solids either. So on a most basic level, the brain’s native structure must be something in between a liquid and a solid. A common way to model the material properties of the brain is via gelatin (Ploch et al., 2016), and an every day example of gelatin is jello.

One type of structure that is between a liquid and a solid is a gel. Gels can also be thought of as a solid with a liquid dispersed throughout it. Gels are a classic type of soft matter that are omnipresent in our lives and in biological systems (Douglas, 2018). As (Douglas, 2018) describes it:

“Gels are ubiquitous—many foods (e.g., gelatin, cheese, ketchup), consumer products (e.g., toothpaste, cosmetics, shaving cream, etc.) and industrial products (e.g., adhesives, asphalt) can be defined as belonging to a rheologically-defined class of materials termed”gels”. Moreover, the gel state is also characteristic of biological materials since the cytoplasm of eukaryotic cells is typically a gel, the cell as a whole has gel-like rheological properties, collagen extracts forms gels similar to those found in the cell extracellular matrix, and, in some cases, gel-like rheology extends to animal tissues composed of ensembles of cells “glued” together by the extracellular matrix, and it is thus not surprising that diverse forms of biological material such as foods, biofilms, soils, etc. have a gel-like nature. Gels are then the quintessential form of soft matter.”

Biological systems are often described as “gel-like” because there might be slight deviations from gel behavior or because the gel behavior may not have been conclusively shown. I often use this terminology of “gel-like” so as to not claim to be more precise than is warranted by the data.

Most organs, including the brain, can be thought of as biological gels or more specifically hydrogels. Hydrogels are gel structures that can maintain their shape when dissolved in water (Liu et al., 2020). The sol phase of a hydrogel is a flowing fluid (Jeong et al., 2012). The gel phase maintains a particular 3D network structure and does not flow or change its shape on practical timescales. So, a hydrogel has solid-like properties despite being contained in a liquid water solvent.

Transition of particles from the sol to gel state; gelierung = German for gelation; Image source

Biological hydrogels tend to be soft, elastic to a degree but also viscous to a degree, and contain a high concentration (60-90%) of water (Liu et al., 2020). As (Liu et al., 2020) notes, the body can be thought of as a living machine made up of mostly skeletons and different types of hydrogels.

Basic terminology of gel-like materials

What is a gel? Of all of the fields I have learned about in writing these essays, perhaps none has less precise definitions than the materials science of gels (Almdal et al., 1993). Defining what a gel is appears to be a Sisyphean task for humanity. I will try to avoid this quagmire by using somewhat non-rigorous, not always consistent definitions and begging forgiveness. As one of the historical pioneers in the field, Dorothy Lloyd, noted:

“The colloidal condition, the”gel”, is one which it is easier to recognize than to define, and even recognition is confused by the fact that the limits between gel and sol, on the one hand, and gel and what may be termed curd, on the other, are not precise, but consist of a gradual change. For this reason some workers classify as “gels” systems which others exclude. Only one rule seems to hold for all gels, and that is that they must be built up from two components, one of which is a liquid at the temperature under consideration, and the other of which, the gelling substance proper, often spoken of as the gelator, is a solid. The gel itself has the mechanical properties of a solid, i.e., it can maintain its form under the stress of its own weight, and under any mechanical stress it shows the phenomenon of strain.” (from Lloyd, 1926, as cited in (Almdal et al., 1993)).

Let’s review some properties of gels so that we can develop mental models of their biophysics. Intuitively, gels have a consistency similar to jello and have a “squishy” response to stress (Douglas, 2018).

One important property of gels is that they are elastic. Elasticity is the propensity of a material to reversibly resist a distorting force by changing its shape and then returning to its original shape when the force is removed – sort of like a trampoline. This is as opposed to plasticity, which refers to the propensity of a material to undergo an irreversible change in shape in response to a force.

One way to quantify the elasticity of a gel is by its Young’s modulus. This quantifies the relationship between the stress (i.e., the force per the area) and the strain (i.e., the amount of deformation) in a material in response to a uniaxial force – meaning a push or pull in one direction relative to the object.

The Young’s modulus is the slope of the linear portion of the stress-strain curve for a material; Source: Nicoguaro

Gels are generally called viscoelastic when they have both viscous and elastic properties. One way to quantify the viscoelasticity of a gel is by its response to an oscillatory force. In response to an oscillatory force, a more elastic gel-like object will exhibit strain that is in phase with the force, so that they occur simultaneously. In contrast, for a more viscous gel-like object, there will be more of a delay between the strain observed and the stress applied. Brain tissue, like that of most soft organs, is viscoelastic because it exhibits both elastic and viscous responses to an oscillatory type of stress.

The storage modulus and loss modulus of a gel-like object quantify the relationship between the stress and the strain in response to an oscillatory force.

The storage modulus:

The loss modulus:

One convention is that during gel formation, a gel is said to have formed when the ratio of the storage modulus to the loss modulus is greater than one. As a gel breaks down over time, it will exhibit more viscous behavior and less elastic behavior.

As the degree of crosslinking in a gel increases, the gel will exhibit more elastic behavior and less viscous behavior. Generally speaking, crosslinking of the constituent molecules is usually required for gel formation; however, there are some exceptions in which crosslinks are not required for gel formation, in which the topological arrangement alone of long, stiff, long-lasting biomolecules such as filamentous proteins can produce gel-like rheological behavior (R. Raghavan et al., 2012).

Highly viscous materials without significant elastic components generally act similar to glass-like materials. On longer time scales, cells in the brain tend to act like glass-like materials, insofar as they demonstrate slow, locally non-elastic rearrangements in response to a force (Deng et al., 2006). This is because on longer time scales, the components that make cells elastic, such as the actin cytoskeleton, are able to actively dissociate and remodel to change their shape in response to a given force (Hohmann et al., 2019).

Yet another important parameter of gels is the yield point. The yield point indicates the location on a stress-strain curve where a material stops demonstrating elastic behavior – temporarily deforming in response to a stress – and begins to demonstrate plastic behavior, which is permanently deforming in response to a stress.

When a gel reaches its yield point, it “melts,” “fluidizes,” or “liquefies” into its component particles or polymers (Douglas, 2018). This phenomenon is known as shear thinning, wherein the viscosity of the material increases as the stress increases. An everyday example of fluidization of a viscous material is ketchup and how it stubbornly refuses to flow out of the bottle until enough stress is applied and it begins to flow out of the bottle rapidly all at once.

The cytoskeleton as a gel

The intracellular space has gel-like properties that appear to be largely due to the cytoskeleton. The human cytoskeleton is primarily made of proteins, which can be categorized into actin, intermediate filaments, microtubules, and supporting proteins such as crosslinkers. The precise protein composition of the cytoskeleton varies significantly based on the cell type. For example, neurons have specialized intermediate filament proteins called neurofilaments, in astrocytes the primary intermediate filament is a protein called GFAP (Pekny et al., 2004), and oligodendrocytes do not have intermediate filaments (Bauer et al., 2009).

On short time scales, actin and microtubule polymers are crosslinked to form a stable mesh by a large number of crosslinking helper proteins. So over short time scales, the cytoskeleton is said to exhibit elastic behavior, meaning that when a stress is applied, the network will bend temporarily but return to its original configuration once the stress is removed (Mogilner et al., 2018).

Over long time scales, the cytoskeletal crosslinks tend to dissociate, meaning that the polymer units can move around and the mesh can remodel prior to crosslinking once again. The turnover of cytoskeletal crosslinking helper proteins is generally in the range of seconds to minutes. So over longer time periods of seconds, the cytoskeleton is said to exhibit viscous behavior (Mogilner et al., 2018).

Importantly, the major type of cytoskeletal remodeling that causes it to display viscous behavior during life, as opposed to elastic behavior, is an active process. For example, one of the main ways that actin filaments break down is via the binding of a particular protein, called ADF-cofilin. This binding increases the local mechanical stress in an actin filament and thereby increases its fragmentation rate (Hohmann et al., 2019).

The binding of ADF-cofilin to actin requires energy in the form of ATP. ATP can be thought of as the molecular unit of currency, because it can be “spent” in order to stimulate chemical reactions in a cell. After clinical death, cellular energy supplies in the form of ATP are rapidly depleted. As a result, once ATP has been depleted after clinical death, this form of regulated breakdown of actin filaments will not occur. The cytoskeleton can still decompose as a result of autolytic processes such as hydrolysis; however, these will likely take a longer amount of time.

Of the three major cytoskeletal proteins, the actin cytoskeleton is often thought to be the primary determinant of the mechanical strength of cells (Ananthakrishnan et al., 2006). One study found that disrupting actin filaments decreases the mechanical strength by threefold, whereas disrupting microtubules or intermediate filaments does not materially change this value (as cited in (Ananthakrishnan et al., 2006)). The cell membrane has a bending rigidity of around 1000 times less than the cortical actin network (as cited in (Ananthakrishnan et al., 2006)).

Schematic of the actin cortex cytoskeleton in axons; (Leite et al., 2016)

Cytoskeletal networks created by neurofilaments, which are the intermediate filaments found in neurons, are also found to be quite stable over time (Yuan et al., 2009). Neurofilaments have unique sidearms that allow them to form parallel arrays that can support the highly polar shapes of neurons. As opposed to actin and microtubules, neurofilaments form a highly stationary and a metabolically stable network – they don’t turn over as much (Mages et al., 2018).

The cytoskeleton tethers the plasma membrane in place through a specialized portion of it called the cell cortex. In oligodendrocytes, which have a specialized wrapping extensive of its plasma membrane called myelin, myelin-specific proteins such as MBP interact closely with cytoskeletal proteins to maintain their association (Bauer et al., 2009).

The proteins that make up the cytoskeleton are properly referred to as biomolecules. Other biomolecules are frequently attached to the cytoskeleton directly through covalent bonds (Mogilner et al., 2018). This is a major form of what we can consider anchoring. It stands to reason that biomolecules which are directly attached to the cytoskeleton will retain their positions longer during the dying process. As far as I can tell, this is generally what is found: for example, proteins embedded in the cell membrane, which is directly attached to the cytoskeleton, are reported to be relatively stable even when profiled some period of time after death in the brain. As (Waldvogel et al., 2006) describe it:

Post-mortem human tissue, due to the nature of its preservation, is unpredictable in its immunohistochemical labeling compared to animal tissue. So far we have not been able to demonstrate that any particular factor such as post-mortem delay, age or sex of the subject, pH or various fixative regimes governs staining ability and antigen preservation in human tissue. It appears more likely to be influenced by the “agonal state” of the subject, that is, the tissue oxygenation and general state of health of the subject leading up to death. Receptor proteins and other proteins which are embedded in the cell membranes are generally stable, and structural proteins such as components of the cytoskeleton are also quite stable, whereas neurotransmitter candidates and certain enzymes are much more labile and less likely to be preserved.

Biomolecules can also be indirectly constrained to particular positions as a result of diffusion restrictions imposed by the cytoskeleton. The diffusion of protein molecules is restricted by the pore size of the actin mesh (Mogilner et al., 2018). The pore size is the amount of space in a 3D network of actin filaments that is devoid of actin.

One study estimates that for actin monomers, the decrease in diffusion speed due to the actin mesh compared to pure water is around an order of magnitude (as cited in (Mogilner et al., 2018)). While this slows down diffusion somewhat, it makes sense that biomolecules only constrained in place by membranes and diffusion constraints due to the cytoskeletal mesh, such as small molecule neurotransmitters, would be less likely to be retained during the dying processes, during which membranes are likely to be damaged.

Gel-like structures all the way down

Consistent with the gel-like structure of cells in general, subcellular areas also contain gel-like structures. For example, the synaptic bouton and post-synaptic density both form gel-like structures (Zeng et al., 2016). Protein clustering of these structures at membranes causes phase separation in a way that seems to be important for their function (Feng et al., 2019).

When a “ribosome” is seen on a microscopy image, that has either been frozen or fixed prior to visualization, what is really being captured on a molecular level is the aggregation of the molecules that comprise the ribosome into a gel-like network in a stereotyped shape and position. When we see the expected shape of this type of aggregation under the microscope, we might say something like “the ribosomes are present.”

Whether a structure can be seen under the microscope after fixation, therefore, is based on the degree of aggregation of its biomolecular components. (Wang et al., 1987) have a nice table of whether different structures are expected to be seen under the microscope, based on their degree of aggregation.

The expected preservation quality of cellular sub-structures depends on their degree of aggregation; (Wang et al., 1987)

My understanding of the reason for this is that the loose structures are weaker gel-like structures or even not gel-like at all. As will be discussed in a later essay, crosslinking-based fixation, which is a key part of processing for routine light microscopy, strengthens and stabilizes gel-like networks through covalent crosslinking of constituent biomolecules. However, if the gel-like network is not strong enough to begin with, then the fixation process will not be able to stabilize it enough to retain it.

One related consequence of fixation is it can potentially introduce gel-like structures that are not necessarily present in native tissue. This is one reason that artifacts can occur in microscopic images of fixed cells. Historically, this may have happened with the “mesosome”, a structure associated with bacterial cell membranes, that only appears in tissue prepared via chemical fixation, but not cryopreservation. However, there appears to still be some uncertainty in the field about whether the mesosome is actually or always an artifact.

Cell membranes in fixed or frozen B. subtilis; (Nanninga, 1971)

Liquid-like and gel-like structures in the cell

Not everything in the cell is best modeled as being a part of a gel-like structure. To explore this, let’s hone in on the nucleolus, a structure in the nucleus that plays a role in making ribosomes (Lafontaine et al., 2021) (Riback et al., 2022).

The nucleolus has been described as a prototypical biomolecular condensate. Biomolecular condensates are organizational structures in cells that lack membranes and have a mixture of liquid-like and solid-like properties. The nucleolus is surrounded by the less dense nucleoplasm. The structure of the nucleolus is still an active area of investigation, and one can imagine which of its potential properties would make it more liquid-like vs solid-like; liquid-like properties would include mixing of its components and exchange of its components with the surroundings (Riback et al., 2022).

Theoretical liquid-like vs solid-like properties of the nucleolus; (Riback et al., 2022)

(Riback et al., 2022) is a study that sheds light on how the nucleolus is organized at the biomolecular level. They use diffusion monitoring techniques in live cells to monitor how biomolecules move about the nucleolus.

One of the major components of the nucleolus is ribosomal RNA, or rRNA for short. Nascent, random coil structured rRNA is much larger in diameter than more mature folded rRNA, as quantified by a higher radius of gyration, Rg. As rRNA matures, it leaves the nucleolus. (Riback et al., 2022) find that random coil rRNA has more aggregation and therefore forms a gel-like structure that endows the nucleolus with higher degrees of gel-like elasticity.

Unfolded rRNA has a higher diameter than folded rRNA, making it more entangled (upper); nascent rRNA endows the nucleolus with more elastic as opposed to viscous properties (lower); (Riback et al., 2022).

While aggregated rRNA in the nucleolus forms a gel-like structure, there are hundreds of other types of biomolecules in the nucleolus. What of them? As discussed in a previous essay, while we can call the whole structure a “nucleolus” when we look at it under the microscope, in reality it is a heterogeneous abstract structural feature that is made up of many component biomolecules.

In this study, they measure a type of nucleolar protein, NPM1, and find that this protein only weakly interacts with the rRNA gel, allowing it to diffuse about at much higher rates than rRNA. As they describe (Riback et al., 2022):

With nascent rRNA chains giving rise to strong entanglement and viscoelasticity, it may be unclear how nucleolar proteins such as NPM1 can exhibit rapid dynamics (e.g. FRAP recovery), which is often mistakenly taken as sufficient evidence of a liquid-like material state… [W]e find that the characteristic timescale for molecular dynamics is ~3 orders of magnitude slower for rRNA compared to NPM1; this is consistent with the vast differences in dynamics recently observed for nascent rRNA and NPM1 mixtures in vitro. Thus, while rRNA forms a slowly-relaxing viscoelastic gel that dominates the bulk material properties of the nucleolar interior, nucleolar proteins are only interacting with this gel transiently, and can apparently diffuse readily through its interstices.

Notably, the ribosomal RPL5 protein was found to have slower molecular dynamics than NPM1, despite being smaller, suggesting that RPL5 is more incorporated into the ribosomal subunit and more strongly anchored to the gel-like structures in the nucleolus.

If we extrapolate the example of the nucleolus to other structures inside and outside of cells, we can come to a principle of cellular organization. Cellular structures are often relatively stabilized as a result of gel-like structures, which are made up of a subset of biomolecule types. Other biomolecule types can be more or less anchored to these gel-like structures through binding interactions, and can therefore exhibit more or less liquid-like dynamics.

Hierarchical organization of biomolecules in cells

In addition to occurring in different phases (more liquid-like or more gel-like), networks of proteins in cells can be conceptualized at different spatial and protein abundance scales. For example, one study built a map of such multi-scale organization using high-resolution protein-protein interaction data (Qin et al., 2021).

Protein-protein interaction map; a node is a “system,” whose sizes correlate with the number of proteins; arrows indicate hierarchical containment; vertical position is based on the predicted diameter; visualized using Cytoscape; downloaded from the associated website of (Qin et al., 2021)

In this map, one can conceptualize the nucleolus as one structure, or the RNA processing complex family as one of the sets of proteins that comprise it, or RNA processing complex 1 as one type of protein complex in the RNA processing complex family.

Each of these networks of protein systems occurs on a different spatial scale and contains a different number of proteins acting in combination. They each may be more or less anchored to gel-like structures.

Cell-cell connections and the extracellular matrix

One could try to imagine a situation in which cells had distinct structures but were floating in a pool of liquid. This might be somewhat the case for some components of the body, like blood or lymph, but it is not true of the brain. Brain cells are highly connected to one another in stable ways through multiple mechanisms. In fact, there is so little space between the near-seamlessly intertwined cells in the brain that it is often difficult for antibodies to diffuse into the deep areas in order to label proteins there (Chung et al., 2013).

One way that brain cells connect to one another is by releasing certain biomolecules (mostly proteins and carbohydrates) into the areas around them, which form a distinct gel-like structure called the extracellular matrix. Cells also create biomolecules that then attach to this extracellular matrix, which are mainly integrin family proteins (Vecino et al., 2016). The extracellular matrix can be thought of as a way of gluing cells together in order to extend the gel-like properties of the cytoskeleton into an extended network across the brain (Douglas, 2018).

One key difference between the extracellular matrix and the cytoskeleton is that polysaccharides molecules (which are a type of carbohydrate) are a larger part of the composition of the extracellular matrix. These structural polysaccharides help strengthen the matrix and help it to resist compressive forces that would otherwise distort the microstructure of the brain (Alberts et al., 2002).

Another way that brain cells connect to one another is through cell-cell adhesion molecules. There are many different types of adhesion molecules, such as cadherin molecules that form links between the actin cytoskeleton of two different cells. Often the cytoskeleton plays a large part in tethering cell adhesion biomolecules to the inside of cells, thus helping to connect the cytoskeleton to the extracellular matrix (Leshchyns’ka et al., 2016):

Schematic of how intracellular domains of certain neural cell-cell adhesion molecules are linked to the cytoskeleton; (Leshchyns’ka et al., 2016)

A particularly important cell-cell connection in the brain is the chemical synapse, which is a key conduit for electrochemical information flow through the connectome. There are hundreds of trillions of synapses in a human brain. Synapses, like every part of the cell, maintain their shape via their cytoskeleton, in particular the actin and neurofilament networks. There are a large number of adhesion molecules that specialize in creating connections at the synapses between neurons (Togashi et al., 2009). Specialized proteins maintain connections to the extracellular matrix (via adhesion molecules such as integrins) and to the other side of the synapse (trans-synaptic adhesion molecules) (Kilinc, 2018):

Schematic of some of the mechanically active biomolecules near the synapse; (Kilinc, 2018)

The extracellular matrix around the synapse is a specialized structure called the perineuronal net that is also thought to regulate synaptic plasticity in a bidirectional way. Other areas of the brain have other types of extracellular matrices, including interstitial matrices and basement membranes around the blood brain barrier.

Macroscopic properties of the brain

As a macroscopic object, the brain generally behaves as a gel as well – albeit quite a soft gel. As discussed above, one way to measure the strength of a gel is by its Young’s modulus. A material’s Young’s modulus is a measure of how much the material will deform when a given uniaxial force is applied. Relative to other organs, the brain has a very low Young’s modulus, measuring approximately 100-1000 Pascals (Budday et al., 2020) (Canovic et al., 2016). In other words, compared to other organs, the stiffness of the brain is low, most similar to adipose tissue or pancreatic tissue.

Stiffness of different organs in the body; (Budday et al., 2020)

The stiffness of brain tissue tends to increase as the myelin content increases (Budday et al., 2020). This is why people with lower levels of myelin, such as children or adults who have had cerebrovascular disease, tend to have more malleable brain tissue on neuropathologic examination of the brain.

What the relatively low Young’s modulus of the brain means is that the brain is particularly viscous and likely to be deformed in response to an external force. Brain tissue is so soft that it noticeably deforms in shape due to gravity (Budday et al., 2020). Left to its own devices, substantial deformation of the brain due to gravity will begin to occur after clinical death.

Brain tissue has a high water content of 80% (Budday et al., 2020). As a result, many of the mechanical properties of brain tissue vary based on whether water is present or has been forced out of the areas between cells. Brain tissue appears to be stiffer when it is undrained, i.e. when water is still present; thus, it is called “biphasic.” For this reason, measurements of brain mechanical and rheological properties are highly sensitive to the amount of fluid present in the brain. Whether water is present or not is likely a key factor to consider when analyzing studies of brain tissue.

Macroscopically, the brain is also highly heterogeneous, which complicates attempts to measure its properties and preserve it with a uniform method:

Heterogeneity of macro- and micro-structures in the brain, Klüver-Barrera stained images at 20x magnification; (Budday et al., 2020)

White matter and grey matter in particular can have highly divergent responses to preservative chemicals, especially cryoprotective agents.

The brain is constantly changing and remaking itself during the living process

Brain structures are not static. They are in a constant state of flux as they break down and are rebuilt. For example, myelin membrane components have a half life on the order of weeks to months (Yeung et al., 2014), during which time they are breaking down and being rebuilt. As another example, actin turnover is also frequent. The half-life of F-actin filaments has been found to be in the range of 10-30 seconds (Yamashiro et al., 2018). Another study has reported that the majority of the actin in dendrites, approximately 81%, turns over every 44 seconds (Engl et al., 2015).

The type of remodeling that occurs during the living process is not generally thought of as causing information theoretic death, at least from an ordinary survival perspective. Therefore, based on first principles alone, we can say that there must be some buffer in how much change can be tolerated after legal death and prior to the initiation of the preservation process, before the brain reaches information-theoretic death.

Conclusions

Instead of being about “brain information preservation,” these essays should perhaps be about “brain gel preservation.” Gel-like structures appear to be the primary organizing and stabilizing substance for the information content of valued cognitive functions in the brain.

This helps us to predict how brain tissue breaks down during the dying process: gel-like structures will begin to decompose and dissolve, until they are fully in solution and no longer have elastic properties at all. It also helps us to understand a molecular approach to preservation: our goal is to stabilize and maintain gel-like networks and their interacting anchored biomolecules in as close as possible to their native structural states over the long-term.

Relative to other organs, the brain has some challenging material properties, such as its softness and its heterogeneity. These properties make the biophysics of preserving the brain more vexing. But we have to play with the cards we have been dealt. It’s important to recognize these challenges so that we use robust methods that will allow for successful preservation despite them.

Further reading

References

Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K. and Walter, P., The Extracellular Matrix of Animals, Molecular Biology of the Cell. 4th Edition, 2002.
Almdal, K., Dyre, J., Hvidt, S. and Kramer, O., Towards a Phenomenological Definition of the Term ‘Gel,’ Polymer Gels and Networks, vol. 1, no. 1, pp. 5–17, January 1993. DOI: 10.1016/0966-7822(93)90020-I
Ananthakrishnan, R., Guck, J., Wottawah, F., Schinkinger, S., Lincoln, B., Romeyke, M., Moon, T. and Käs, J., Quantifying the Contribution of Actin Networks to the Elastic Strength of Fibroblasts, Journal of Theoretical Biology, vol. 242, no. 2, pp. 502–16, September 2006. DOI: 10.1016/j.jtbi.2006.03.021
Bauer, N. G., Richter-Landsberg, C. and Ffrench-Constant, C., Role of the Oligodendroglial Cytoskeleton in Differentiation and Myelination, Glia, vol. 57, no. 16, pp. 1691–1705, 2009. DOI: 10.1002/glia.20885
Budday, S., Ovaert, T. C., Holzapfel, G. A., Steinmann, P. and Kuhl, E., Fifty Shades of Brain: A Review on the Mechanical Testing and Modeling of Brain Tissue, Archives of Computational Methods in Engineering, vol. 27, no. 4, pp. 1187–1230, September 2020. DOI: 10.1007/s11831-019-09352-w
Canovic, E. P., Qing, B., Mijailovic, A. S., Jagielska, A., Whitfield, M. J., Kelly, E., Turner, D., Sahin, M. and Van Vliet, K. J., Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry, Journal of Visualized Experiments : JoVE, no. 115, p. 54201, September 2016. DOI: 10.3791/54201
Chung, K. and Deisseroth, K., CLARITY for Mapping the Nervous System, Nature Methods, vol. 10, no. 6, pp. 508–13, June 2013. DOI: 10.1038/nmeth.2481
Deng, L., Trepat, X., Butler, J. P., Millet, E., Morgan, K. G., Weitz, D. A. and Fredberg, J. J., Fast and Slow Dynamics of the Cytoskeleton, Nature Materials, vol. 5, no. 8, pp. 636–40, August 2006. DOI: 10.1038/nmat1685
Douglas, J. F., Weak and Strong Gels and the Emergence of the Amorphous Solid State, Gels (Basel, Switzerland), vol. 4, no. 1, p. E19, February 2018. DOI: 10.3390/gels4010019
Engl, E. and Attwell, D., Non-Signalling Energy Use in the Brain, The Journal of Physiology, vol. 593, no. 16, pp. 3417–29, August 2015. DOI: 10.1113/jphysiol.2014.282517
Feng, Z., Chen, X., Wu, X. and Zhang, M., Formation of Biological Condensates via Phase Separation: Characteristics, Analytical Methods, and Physiological Implications, The Journal of Biological Chemistry, vol. 294, no. 40, pp. 14823–35, October 2019. DOI: 10.1074/jbc.REV119.007895
Hohmann, T. and Dehghani, F., The Cytoskeleton, Cells, vol. 8, no. 4, p. 362, April 2019. DOI: 10.3390/cells8040362
Ikeda, S. and Foegeding, E. A., Measurement of Gel Rheology: Dynamic Tests, Current Protocols in Food Analytical Chemistry, vol. 7, no. 1, pp. H3–2, 2003.
Jeong, B., Kim, S. W. and Bae, Y. H., Thermosensitive Solgel Reversible Hydrogels, Advanced Drug Delivery Reviews, vol. 64, pp. 154–62, December 2012. DOI: 10.1016/j.addr.2012.09.012
Kilinc, D., The Emerging Role of Mechanics in Synapse Formation and Plasticity, Frontiers in Cellular Neuroscience, vol. 12, 2018.
Lafontaine, D. L. J., Riback, J. A., Bascetin, R. and Brangwynne, C. P., The Nucleolus as a Multiphase Liquid Condensate, Nature Reviews Molecular Cell Biology, vol. 22, no. 3, pp. 165–82, March 2021. DOI: 10.1038/s41580-020-0272-6
Leite, S. C. and Sousa, M. M., The Neuronal and Actin Commitment: Why Do Neurons Need Rings?, Cytoskeleton, vol. 73, no. 9, pp. 424–34, 2016. DOI: 10.1002/cm.21273
Leshchyns’ka, I. and Sytnyk, V., Reciprocal Interactions Between Cell Adhesion Molecules of the Immunoglobulin Superfamily and the Cytoskeleton in Neurons, Frontiers in Cell and Developmental Biology, vol. 4, 2016.
Liu, X., Liu, J., Lin, S. and Zhao, X., Hydrogel Machines, Materials Today, vol. 36, pp. 102–24, June 2020. DOI: 10.1016/j.mattod.2019.12.026
Mages, B., Aleithe, S., Altmann, S., Blietz, A., Nitzsche, B., Barthel, H., Horn, A. K. E., et al., Impaired Neurofilament Integrity and Neuronal Morphology in Different Models of Focal Cerebral Ischemia and Human Stroke Tissue, Frontiers in Cellular Neuroscience, vol. 12, 2018.
Mogilner, A. and Manhart, A., Intracellular Fluid Mechanics: Coupling Cytoplasmic Flow with Active Cytoskeletal Gel, Annual Review of Fluid Mechanics, vol. 50, no. 1, pp. 347–70, 2018. DOI: 10.1146/annurev-fluid-010816-060238
Nanninga, N., The Mesosome of Bacillus Subtilis as Affected by Chemical and Physical Fixation, The Journal of Cell Biology, vol. 48, no. 1, pp. 219–24, January 1971. DOI: 10.1083/jcb.48.1.219
Pekny, M. and Pekna, M., Astrocyte Intermediate Filaments in CNS Pathologies and Regeneration, The Journal of Pathology, vol. 204, no. 4, pp. 428–37, November 2004. DOI: 10.1002/path.1645
Ploch, C. C., Mansi, C. S. S. A., Jayamohan, J. and Kuhl, E., Using 3d Printing to Create Personalized Brain Models for Neurosurgical Training and Preoperative Planning, World Neurosurgery, vol. 90, pp. 668–74, June 2016. DOI: 10.1016/j.wneu.2016.02.081
Qin, Y., Huttlin, E. L., Winsnes, C. F., Gosztyla, M. L., Wacheul, L., Kelly, M. R., Blue, S. M., et al., A Multi-Scale Map of Cell Structure Fusing Protein Images and Interactions, Nature, vol. 600, no. 7889, pp. 536–42, December 2021. DOI: 10.1038/s41586-021-04115-9
R. Raghavan, S. and F. Douglas, J., The Conundrum of Gel Formation by Molecular Nanofibers, Wormlike Micelles, and Filamentous Proteins : Gelation Without Cross-Links?, Soft Matter, vol. 8, no. 33, pp. 8539–46, 2012. DOI: 10.1039/C2SM25107H
Riback, J. A., Eeftens, J. M., Lee, D. S. W., Quinodoz, S. A., Beckers, L., Becker, L. A., and Brangwynne, C. P., Viscoelastic RNA Entanglement and Advective Flow Underlie Nucleolar Form and Function, January 2022.
Togashi, H., Sakisaka, T. and Takai, Y., Cell Adhesion Molecules in the Central Nervous System, Cell Adhesion & Migration, vol. 3, no. 1, pp. 29–35, 2009.
Vecino, E. and Kwok, J. C. F., The Extracellular Matrix in the Nervous System: The Good and the Bad Aspects, Composition and Function of the Extracellular Matrix in the Human Body, IntechOpen, 2016.
Waldvogel, H. J., Curtis, M. A., Baer, K., Rees, M. I. and Faull, R. L. M., Immunohistochemical Staining of Post-Mortem Adult Human Brain Sections, Nature Protocols, vol. 1, no. 6, pp. 2719–32, 2006. DOI: 10.1038/nprot.2006.354
Wang, N. S. and Minassian, H., The Formaldehyde-Fixed and Paraffin-Embedded Tissues for Diagnostic Transmission Electron Microscopy: A Retrospective and Prospective Study, Human Pathology, vol. 18, no. 7, pp. 715–27, July 1987. DOI: 10.1016/s0046-8177(87)80243-5
Yamashiro, S., Tanaka, S., McMillen, L. M., Taniguchi, D., Vavylonis, D. and Watanabe, N., Myosin-Dependent Actin Stabilization as Revealed by Single-Molecule Imaging of Actin Turnover, Molecular Biology of the Cell, vol. 29, no. 16, pp. 1941–47, August 2018. DOI: 10.1091/mbc.E18-01-0061
Yeung, M. S. Y., Zdunek, S., Bergmann, O., Bernard, S., Salehpour, M., Alkass, K., Perl, S., et al., Dynamics of Oligodendrocyte Generation and Myelination in the Human Brain, Cell, vol. 159, no. 4, pp. 766–74, November 2014. DOI: 10.1016/j.cell.2014.10.011
Yuan, A., Sasaki, T., Rao, M. V., Kumar, A., Kanumuri, V., Dunlop, D. S., Liem, R. K. and Nixon, R. A., Neurofilaments Form a Highly Stable Stationary Cytoskeleton After Reaching a Critical Level in Axons, Journal of Neuroscience, vol. 29, no. 36, pp. 11316–29, September 2009. DOI: 10.1523/JNEUROSCI.1942-09.2009
Zeng, M., Shang, Y., Araki, Y., Guo, T., Huganir, R. L. and Zhang, M., Phase Transition in Postsynaptic Densities Underlies Formation of Synaptic Complexes and Synaptic Plasticity, Cell, vol. 166, no. 5, pp. 1163–1175.e12, August 2016. DOI: 10.1016/j.cell.2016.07.008