Soft Matter

Liquid-liquid phase separation (LLPS) driven by intrinsically disordered protein regions underlies the formation of membraneless organelles. The role of these structures in cell biology is an active area of research, yet their physical understanding remains less developed than that of protein assemblies with well-defined, rigid tertiary structures. The amino acid composition of disordered regions, particularly the abundance of charged residues, is known to modulate phase behavior. These factors govern accessible conformations, the assembly of compact sticker clusters, and the porosity of the resulting polymer networks. In contrast, the spatial distribution of side-chain volumes remains a comparatively understudied sequence determinant. Here, we employ a model that captures the patterning of side-chain volumes and interaction strengths without specifying chemical identity to show how sequence blockiness modulates the phase-nucleation propensity, connectivity, and compactness of polypeptide-like polymer assemblies. Using a library of polymers with identical composition but permuted residue arrangements, we show that clustering of bulky residues generates cooperative sticker domains and identify the threshold blockiness required to stabilize polymer networks. Coarse-grained simulations and mean-field theory reveal that this effect arises from an interplay among steric repulsion, solvent entropy, and higher-valency interactions, whereby blocky patterns promote the formation of multi-node percolated networks. Segregation of stickers and spacers is therefore one of the sequence parameters governing LLPS which may be useful for interpreting the evolution of disordered protein sequences, high-throughput proteome screening, and engineering of stimuli-responsive biomaterials. These rules may be exploited to predetermine condensate stability, viscoelasticity, and molecular exchange rates without altering chemical functionality.

Phase separating biopolymers are often described by the sticker-spacer model, which accounts for non-attractive linkers that connect discrete attractive moieties. "Associative polymer" theories describe these systems as two coexisting fluids of stickers and spacers, respectively. In such theories the stickers and spacers are decoupled in the sense that sticker binding does not affect spacer conformations or vice versa. Informed by coarse-grained simulations, we develop a phenomenological mean-field theory to determine the microscopic coupling between the sticker and spacer degrees of freedom. Accounting for linkers connecting adjacent spacers introduces free energy contributions due to conformational entropy of the spacer and the binding combinatorics of the stickers. These contributions have opposite dependencies on spacer length because longer spacers allow a greater volume to search for binding partners but incur a greater entropy cost upon bond formation. The length dependent terms from these contributions cancel leaving a length-independent elastic term arising from the fact that a small amount of spacer stretching lowers the free energy by increasing the number of possible binding partners. The cancellation of spacer length from the theory justifies the decoupling approximation and supports mean-field treatments of heterogeneous spacers.

Cell membranes are decorated by dense, polymeric macromolecules such as proteins with bulky intrinsically disordered regions, glycosylated proteins, and glycopolymers. Experiments have illustrated that the curvature of cellular membranes, which plays a role in diverse biological processes, can be regulated by the local density of these membrane-tethered polymers and their response to environmental changes. However, the mechanisms underlying the feedback between solution conditions, membrane curvature, and the local density and molecular weight of membrane-tethered polymers remain largely unexplored. In this work, using a combination of theory and experiment, we show that the ionic strength of the surrounding solution and the degree of ionization both play important roles in regulating the conformation of the membrane-tethered polymers, which in turn influences their membrane curvature generation capabilities. Specifically, we show that for negatively charged, disordered proteins and glycans, the thickness of the polymer brush decreases with ionic strength but increases with grafting density and degree of ionization, resulting in enhanced sensitivity to membrane curvature and an amplified ability to drive membrane curvature at lower ionic concentrations. Our findings reveal a mechanism by which membrane-tethered polymers generate membrane curvature to minimize free energy, with the extent of curvature generation modulated by the polymer grafting density and layer thickness, as well as by the ionic strength of the surrounding solution. The implications of these findings are broad, suggesting that perturbations to biopolymer charge, such as sialylation of glycans and phosphorylation of disordered proteins, could actively modulate membrane curvature induction in diverse cellular processes from endocytosis to cell motility.

Nanoplastic interactions with cell membranes can influence particle uptake, translocation, and biological effects, making their molecular description important for assessing the environmental and biological consequences of plastic pollution. However, molecular simulations generally represent nanoplastics using monodisperse polymer chains, whereas nanoplastics formed by environmental degradation are polydisperse. Here, we construct 4 nm polyethylene terephthalate (PET), polyethylene (PE), and polystyrene (PS) nanoparticles with lognormal molecular-weight distributions reported for degraded polymers, and with melt-like chain packing and entanglement. Using Martini coarse-grained molecular dynamics, we simulate their interactions for 10 microseconds with three lipid bilayers of increasing complexity. PE and PS insert rapidly and remain largely intact, whereas PET remains surface-associated and progressively releases individual chains. Fluid, polyunsaturated lipid environments undergo greater deformation and promote substantially more chain release, up to 56% of the chains for PET and 43% for PS. Release begins with the shortest chains and progressively extends to longer chains, while semicrystalline PE remains largely intact. In contrast, a size-matched monodisperse PS particle releases no chains and minimally perturbs the membrane. These results identify the molecular-weight distribution as a key determinant of nanoplastic-membrane interactions, and indicate that uniform model particles underestimate the release of polymer chains into biological membranes.

The actin cytoskeleton drives shape changes and transport in cells and supports mechanical signal transmission. However, how cells control and use active cytoskeletal flows at the mesoscale is not well understood. We reconstituted an active cytoskeleton in water-in-oil emulsion droplets of Xenopus laevis egg extract and observed, above a critical droplet size, the emergence of a 3D radially convergent steady-state flow of polymeric actin, maintained by continuous actin turnover. The flow condensed lipid-rich cellular debris into a centered inclusion. Steady-state F-actin density and flow velocity profiles roughly collapse onto scale-invariant master curves. This behavior can be explained by a physical model representing the network as an isotropic active viscous fluid with a percolation threshold. The contracting network behaves as an active swimmer with complex internal dynamics that centers itself and the central inclusion inside the droplets without physical boundary attachment. Active contraction, crosslinking and polymerization dynamics in an actin network can thus generate cell-scale flow patterns that sense the confining geometry and external signals and exert forces that are likely sufficient to move and localize organelles in cells.

Clathrin-mediated endocytosis (CME) is the primary pathway for internalization of extracellular and membrane cargo in eukaryotic cells. It is characterized by a patch of the plasma membrane invaginating and pinching off to generate a cytoplasmic vesicle. Recently, experimental evidence has begun to accumulate in support of proteins assembling into liquid-like condensates at nascent CME sites, but potential functional consequences are not understood. Given that condensate capillary forces can influence membrane bending, we constructed complementary analytical and numerical model systems to probe the mechanical influence of a protein droplet on endocytosis. Model evaluations and analysis reveal that a droplet can regulate the progression of endocytosis by stalling invagination until the droplet grows past a threshold size. After the droplet-mediated brake is released, the droplet provides a force that can drive the membrane bud toward scission. Thus, we propose a ``mechanical timer'' mechanism as a function for protein phase separation in cellular membrane trafficking.

Biofilm mechanics depend strongly on growth history, hydration, nutrient availability, and interfacial attachment, yet conventional rheology requires harvesting and transfer that can perturb the as-grown structure. Here, we introduce bioRheoFlow, a 3D-printed rheometer-mounted platform for cultivation and mechanical characterization without sample transfer. The platform combines evaporation compensation with static or axial nutrient flow, and benchmark measurements validate its rheometric performance. Applied to Pseudomonas putida and Pantoea sp., bioRheoFlow reveals that biofilms cultivated in situ under axial flow exhibit substantially higher elastic moduli than conventionally grown and scraped samples. Despite similar linear-viscoelastic stress ranges, the species show distinct nonlinear responses: P. putida exhibits a lower crossover stress and more abrupt failure, whereas Pantoea sp. sustains larger stresses and deformations. Creep suggests contributions from bulk deformation and biofilm-substrate interactions. bioRheoFlow provides a controlled framework for probing growth-conditioned mechanics in living biofilms.

Large shear deformation in cartilage can lead to tissue failure and injury, which are often precursors to diseases such as post-traumatic osteoarthritis. Under shear, cartilage strain-stiffens prior to failure, which makes stiffening an important indicator of changes to the loading of the extracellular matrix that gives rise to this tissue's properties. While a variety of stiffening mechanisms ranging from uncrimping to fiber-alignment have been explored, whether such mechanisms apply to the depth dependent shear strain stiffening in articular cartilage remains poorly understood. Here, we present experiments and theory that support the idea that the strain-stiffening behavior is caused by a mechanical phase transition from a random isotropic network whose mechanics arise primarily from bending-dominated modes to one where the mechanics arise from stretching-dominated modes. Indeed, consistent with network model predictions, we find that stress-strain data for all depths can be collapsed onto a single universal curve that diverges at a depth-dependent critical strain. These results show that increasing the local concentration of collagen, which constitutes the primary load-bearing network in cartilage, decreases the critical strain at which the strain-stiffening transition occurs, while increasing the low-strain elastic modulus. Furthermore, we demonstrate that bulk compression leads to a higher critical strain and decreased low-strain elastic modulus. Network simulations capture this rich behavior, and indicate that under compression the network collapses, which allows for larger strains before the transition from bending-dominated to stretching-dominated mechanics occurs. Collectively, these findings shed light on the roles that tissue composition and axial strain play in the mechanisms that organize how articular cartilage stiffens during the lead-up to injury.

Biofilm formation--the establishment of cellular communities within a self-secreted polymer matrix--is a behavior exhibited by almost all species of bacteria. Bacterial biofilms are well described as viscoelastic materials, with properties that grant physical advantages, such as elastic resilience and viscous adaptability. Additionally, many biofilm-forming species produce a subpopulation of highly resilient spores. In the model biofilm- and spore-forming species Bacillus subtilis, sporulation and matrix production are regulated by a common gene pathway, provoking the question of how sporulation influences biofilm viscoelastic properties. Investigating this point will facilitate the management of both salutary and deleterious biofilms, especially through manipulating their establishment and dispersion. Here, we investigate the interplay of sporulation and viscoelastic properties using rheological measurements of B. subtilis biofilms with varied matrix and spore production. We find that, to a significantly greater extent than matrix production, biofilm physical development is strongly altered by activation of sporulation through the spo0A regulation pathway. Spore-deficient strains initially establish physically robust biofilms with linear viscoelastic properties and recovery behavior distinct from spore-producing biofilms. However, spore-deficient biofilms fail to maintain their physical properties over time. In contrast, spore-producing wild-type and matrix-mutant biofilms exhibit more stable physical properties. Our results demonstrate that cell-level gene regulation is associated with macroscopic mechanical transitions in cellular communities, with sporulation granting not only a cellular-level survival advantage, but also greater community-level physical stability.

Cardiomyocyte contraction is essential for the pumping action of the heart and deteriorates after myocardial damage, either as a consequence of irreversible cardiomyocyte injury or stiffening of the extracellular matrix, a process described as fibrosis. Cell geometry and substrate stiffness not only influence sarcomere architecture and contractility, they also determine how much work the cardiomyocytes can transfer to their environment. Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes provide a model system to examine these effects in a controlled manner, enabling us to mimic key features of the cardiac environment in vitro. Here we show that geometric confinement and substrate stiffness influence different aspects of cardiomyocyte force generation. By combining traction force microscopy and live-cell imaging of hiPSC-derived cardiomyocytes on soft and patterned substrates, we find that geometric confinement leads to longer resting sarcomeres and more pronounced sarcomere shortening on substrates with both physiological (10 kPa) and fibrotic (30 kPa) stiffness. Interestingly, the substrate stiffness itself has little effect on resting sarcomere length, but influences the peak contractile stress. On 10 kPa substrates, confined cells generate higher peak contractile stresses than unconfined cells, whereas this difference is not observed at 30 kPa. Confinement also results in faster mechanical relaxation at 10 kPa, with no detectable difference at 30 kPa. The combination of defined cell geometry and physiological stiffness leads to longer resting sarcomere lengths, more pronounced sarcomere shortening, higher peak contractile stress, and faster relaxation, features associated with a more mature cardiomyocyte phenotype. These findings show that the mechanical consequences of cell geometry depend on substrate stiffness and, therefore, both aspects have to be considered when employing hiPSC-derived cardiomyocytes as a model system in mechanobiology research, disease modeling and drug testing.

Liquid-liquid phase separation (LLPS) of biomacromolecules has been shown to influence a large variety of biological processes via the formation of membraneless organelles. Aberrant LLPS, however, has been implicated in a number of oncological and neurodegenerative disorders. Proteins related to Alzheimer's, Parkinson's diseases and other major amyloidoses have all been shown to form these condensates. Interestingly, some amyloidogenic proteins were demonstrated to form heterotypic droplets, where both molecules coexisted within the same condensate and affected each other's aggregation. While such cross-interactions have been reported numerous times under non-LLPS conditions, the heterotypic droplet assemblies were only shown for a very limited number of amyloidogenic proteins. In this work, we examine heterotypic LLPS of prion proteins and the pro-inflammatory S100A9, whose cross-interaction under non-LLPS conditions we confirmed in a previous work. The results show that this interaction under condensation-promoting conditions enhances their aggregation, leading to the formation of a higher concentration of amyloid structures. These findings suggest that the pro-inflammatory S100A9 may have a critical role in promoting prion protein aggregation via the assembly of heterotypic condensates under high molecular crowding conditions.

Biomolecular condensates are condensed assemblies of biomolecules that form through the process of liquid phase separation. Condensates in biology typically function as membraneless organelles, providing compartmentalization in the absence of a dividing lipid membrane. The molecular make-up of different condensates includes diverse multivalent proteins and nucleic acids as the primary drivers, many of them including significant fractions of intrinsically disordered regions (IDRs). To date, the sequence to phase separation relationship of IDRs has focused largely on one protein at a time, studying single-component condensate formation, or single-component partitioning into condensates. The co-phase separation and mixing of two or more IDRs is considerably more complex as both sequences can vary widely in their self- and cross-interactions, as well as their relative abundance in solution. It is unclear that a rule which predicts how a single sequence behaves will also predict what happens when two sequences are mixed. In this study, we disentangle the influence of sequence from that of composition using a set of 18 LAF-1 RGG variants that keep the same length and amino-acid composition and change only the order of the residues. This lets us vary charge patterning and, to a lesser extent, hydropathy patterning while keeping protein composition fixed. By themselves, the sequences phase separation and single-chain compaction are controlled by their degree of charge and hydropathy patterning. Within single-component condensed phases, each sequence adopts a more extended conformational ensemble, due to a more favorable, selfsolvated environment. We find that mixing two IDRs together into a condensate causes this universal scaling behavior to break, impacted by the relative interactions of the two components and overall composition of the slab. We find two different qualitative behaviors, one characterized by cooperative co-condensation when both sequences are subcritical, and the other by scaffold-client behavior when one sequence is supercritical. The scaffold-client systems generally show a high degree of demixing, while the co-condensing systems are generally quite well-mixed in the dense phase. This is surprising because even in cases where both partners have significantly different patterning parameters, they still mix. Thus, the descriptor that predicts a sequence's behavior alone can help indicate whether it will mix with or separate from a second component, but it does not fully determine the outcome on its own.

Amyloid {beta}-protein (A{beta}), which is known for its role in Alzheimer's disease (AD), is also involved in several physiological functions. Of the two predominant isoforms, A{beta}40 and A{beta}42, the latter is more aggregation-prone and more active in health and disease. In the brain, A{beta} interacts with free lipids, which may affect its assembly pathways. Here, thioflavin T (ThT) fluorescence spectroscopy is combined with molecular dynamics (MD) to investigate A{beta}40 and A{beta}42 co-assembly with free dimyristoylphosphatidylcholine (DMPC) and DMPC/cholesterol (CHOL) lipids. ThT measurements at several protein-to-lipid (PL) ratios show isoform- and lipid composition-dependent kinetics of A{beta} fibril formation with clear isoform-dependent lipid effects at a PL ratio of 1:5. Early co-assembly stages of six A{beta}40/A{beta}42 with DMPC and DMPC/CHOL lipids at this PL ratio are then examined by MD. MD results indicate that lipids alter the secondary structure more and interact more avidly with A{beta}40 than with A{beta}42. In turn, A{beta}40 increases the planar order of lipid bicelles more than A{beta}42. Unlike A{beta}40, A{beta}42 decreases the nematic order of CHOL. In DMPC/CHOL lipids, A{beta}42 but not A{beta}40 exhibits two A{beta}-lipid interaction modes associated with mutually exclusive A{beta}42-lipid and A{beta}42-A{beta}42 interactions. Our MD findings provide a plausible explanation for an experimentally observed increase in the lag time for A{beta}40 fibril formation in DMPC lipids and even more so in DMPC/CHOL lipids that contrasts with much smaller effects of lipids on A{beta}42 fibril formation and offer additional atomistic-level insights into A{beta} co-assembly with lipids amenable to experimental testing.

Lipid nanoparticles (LNPs) are a leading drug delivery platform for mRNA or other nucleic acid-based therapeutics. LNP platforms are typically composed of ionizable aminolipids, phospholipids, sterols, and lipids tagged with polyethylene glycol. The ionizable aminolipids are typically cationic at low pH, allowing them to encapsulate negatively charged mRNA. The pH at which the ionizable head groups change charge states, the apparent pKa, is crucial to understand the characteristics and formulation of LNP. However, this is difficult to measure as the apparent pKa differs from the pKa of the aminolipids in bulk solution due to the complex chemical and charge environment. In this study, we use molecular dynamics (MD) simulations with alchemical free energy perturbation to calculate the pKa shift as a function of lipid composition, treating LNP-like and vesicle-like environments. Our results show that the pH-dependent structure of the systems affects the trends in pKa shifts in complex ways, driven by factors including surface electrostatics and lipid internalization. Our all-atom simulation protocol allows us to efficiently estimate the apparent pKa of LNP-like models formed with novel ionizable aminolipids and estimate how it will change with composition.

The actin cytoskeleton is a self-assembled material that acts as the mechanical scaffold of the cell. Within the cell, protein cross-linkers organize actin filaments into assemblies with various microstructures that support distinct physiological functions. These protein cross-linkers differ in structural features. To decouple the role of individual cross-linker properties in actin assembly microstructure and mechanics, we synthesize programmable cross-linkers using a DNA linker coupled to binding domains. We find that high affinity cross-linkers induce actin bundling, while low affinity cross-linkers form actin networks. Myosin II motors remodel and contract all cross-linked networks tested in distinct ways from uncross-linked networks. We find that networks with high affinity cross-linkers have different myosin induced dynamics than networks with low affinity cross-linkers. This research expands our understanding of how cells regulate their mechanics and microstructures, while also providing insight towards the structure-property relationship in complex soft materials.

Nanobubbles (NBs) have been widely investigated as ultrasound contrast and therapeutic agents, yet whether submicron gas bubbles can directly generate measurable ultrasound signals remains controversial. Because acoustic scattering scales with bubble size, several studies have recently attributed nanobubble echogenicity primarily to larger bubbles. A recent Science Advances publication, attributed the loss of ultrasound signal after filtration to the removal of residual larger bubbles [1]. This interpretation assumes that filtration acts solely as a size-selection process. Here, we show that filtration also alters the shell properties and stability of lipid-coated NBs, independent of size. Filtered NBs (F-NBs) show significant lipid loss and accumulation on the filters themselves, resulting in decreased bubble stability and greater susceptibility to destruction under acoustic excitation. To decouple size from processing effects, we generated NB populations size-matched to filtered samples using increased centrifugation (IC-NBs), a comparatively gentler separation method. Despite comparable size distributions, IC-NBs retained sustained ultrasound scattering, whereas filtered NBs showed significantly reduced acoustic persistence. Filtration through 5 m pores, ~5-fold larger than the largest bubbles, preserved acoustic persistence but still reduced bubble concentration by ~30%, suggesting that membrane contact may contribute to bubble loss beyond size exclusion. Complementary lipid and gas-content assays provided further evidence of filtration-induced changes. The 0.45 m filtration removed substantially more total lipid than could be attributed to free-lipid removal alone, while the lipid associated with the larger bubbles removed during filtration accounted for only ~5% of the additional lipid loss. Encapsulated gas content remained comparable to that of size-matched centrifuged controls, suggesting that the reduced acoustic stability of filtered NBs is more closely associated with changes in lipid content than with loss of the gas core. These results indicate that membrane filtration is not a purely size-selective process: it removes substantially more lipid than size exclusion predicts and yields bubbles that are less stable under ultrasound, most likely through changes in shell lipid content or in the surrounding lipid reservoir, underscoring the importance of processing-induced effects in interpreting nanobubble echogenicity.

Membrane tension, defined as the energy cost to expand the membrane surface area, is a fundamental mechanical property of the lipid bilayer and an important modulator of various cellular processes. Liposomes provide model systems to study protein-mediated membrane dynamics, where membrane tension can be controlled by osmotic pressure and micropipette aspiration. However, it is challenging to individually control membrane tension of nanometer-sized liposomes, such as small unilamellar vesicles (SUVs), that are widely used for modeling subcellular membrane structures and drug delivery. To bridge this technological gap, we present reconfigurable DNA origami rings for dynamically controlling membrane tension of sub-100 nm liposomes. The DNA nanorings template the formation of uniformly sized SUVs and undergo trigger-responsive dilation and contraction, thus applying controlled and reversible mechanical stress to the SUV membranes. Electron microscopy analyses show deformed liposomes with ~2.5% expanded membrane area following ring dilation. The corresponding increase in membrane tension opens a mechanosensitive ion channel MscL with gating tension of ~10 mN/m. Selectively manipulating a mixture of SUV populations enables timed release of their distinct molecular cargos, either concurrently or one at a time. Furthermore, vesicles with expanded membranes are more conducive to SNARE-mediated fusion. The nanomechanical device thus provides a programmable platform for engineering the mechanics of synthetic nano-vesicles and studying membrane tension modulated processes at the molecular level.

Known for their rapid water surface locomotion, whirligig beetles (Gyrinidae) are also fast divers. They can transition from the surface to underwater swimming in under 100 ms. Diving requires them to generate sufficient hydrodynamic torque to break the water surface and reorient the body. However, the mechanism by which whirligigs create the necessary pitch-down torque is unknown. Here, we show that their diving is achieved by bending the abdomen. The inertial hydrodynamic pressure on the flexed abdomen generates the torque necessary for surface breaking and body rotation. The torque is maximized at an abdominal angle of 45{degrees}, whereas for effective diving, whirligigs display a preferential abdominal angle of around 28{degrees}, closely aligning with the angle that maximizes the torque-to-drag ratio. This abdominal angle is optimized by balancing torque generation with the drag penalty. Our findings inspire a mechanism for surface-to-underwater transition, offering design principles for multimodal robots capable of locomotion across the air-water interface.

Lipid packing defects expose hydrophobic regions of membranes and influence protein-membrane recognition, membrane remodeling, and lipid sensing. Quantitative analysis of these defects remains challenging for heterogeneous membranes with strong curvature, mixed bilayer and monolayer organization, and substantial neutral-lipid content. Here, we present Lipid Defect Analyzer (LipiDeA), an open-source Python framework for quantitative analysis of lipid packing defects in complex membrane systems. LipiDeA reconstructs membrane surfaces using triangulated meshes that adapt to local membrane geometry and identifies packing defects from lipid coverage of the reconstructed surface. The framework further classifies bilayer and monolayer regions, resolves defect properties according to local membrane curvature, and distinguishes defects associated with phospholipid tails and neutral lipids. Validation using planar and buckled DPPC bilayers reproduced previously reported packing defect constants and showed that defects preferentially occur in positively curved membrane regions. Application to lipid-droplet-like membranes revealed composition- and topology-dependent defect formation, with the largest defects occurring during the transition from bilayer-dominated to monolayer-containing membranes and distinct defect properties for DAG- and TAG-containing systems. These results highlight the importance of local membrane geometry and composition in determining lipid packing defects.

Despite lacking a dedicated structural function, brain tissue is exposed to severe mechanical loading during accidents and trauma, with potentially permanent or lethal consequences. Therefore, characterizing its mechanical behavior assumes particular importance for designing protective equipment, planning surgeries, and training neurosurgeons, tasks that often rely on physical brain replicas made from phantom materials. Although the mechanics of brain tissue has been extensively studied, the selection of suitable phantom materials still relies on simplified metrics that fail to capture the highly nonlinear and rate-dependent behavior of the brain. In this work, we characterize the mechanical response at low-to-medium strain rates of common synthetic brain phantom materials, including Sylgard 527, Ecoflex Gel, and a composite hydrogel, and establish rigorous comparisons with porcine brain tissue as a benchmark. To this end, we employ a multi-step stress relaxation protocol under both uniaxial extension and compression across two different strain rates, introduce thermodynamic metrics, and perform comparisons based on rigorous statistical analysis. In particular, we isolate the thermodynamic equilibrium stress-strain curves, representing the baseline material's response, from rate-dependent dissipative inelastic effects, relatable to viscoelastic and poroelastic mechanisms. Our results provide a thorough comparison, revealing critical limitations in existing simulant materials and highlighting significant space for developing improved brain surrogates. The comprehensive dataset collected can also support the calibration of constitutive models, enhancing the predictive accuracy of numerical simulations involving brain tissue and its surrogates. More broadly, this study highlights the need to move beyond simplistic, scalar-metric-based comparisons of mechanical responses across materials.