Unraveling the Local Relation Between Tissue Composition and Human Brain Mechanics Through Machine Learning

The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain

Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.

Heterogeneity of brain extracellular matrix and astrocyte activation

From the blood brain barrier to the synaptic space, astrocytes provide structural, metabolic, ionic, and extracellular matrix (ECM) support across the brain. Astrocytes include a vast array of subtypes, their phenotypes and functions varying both regionally and temporally. Astrocytes' metabolic and regulatory functions poise them to be quick and sensitive responders to injury and disease in the brain as revealed by single cell sequencing. Far less is known about the influence of the local healthy and aging microenvironments on these astrocyte activation states. In this forward-looking review, we describe the known relationship between astrocytes and their local microenvironment, the remodeling of the microenvironment during disease and injury, and postulate how they may drive astrocyte activation. We suggest technology development to better understand the dynamic diversity of astrocyte activation states, and how basal and activation states depend on the ECM microenvironment. A deeper understanding of astrocyte response to stimuli in ECM-specific contexts (brain region, age, and sex of individual), paves the way to revolutionize how the field considers astrocyte-ECM interactions in brain injury and disease and opens routes to return astrocytes to a healthy quiescent state.

A microscale 3D organ on a chip for recapitulating reciprocal neuroendocrine crosstalk between the hypothalamus and the pituitary gland

Conventional 2D or even recently developed 3Din vitroculture models for hypothalamus and pituitary gland cannot successfully recapitulate reciprocal neuroendocrine communications between these two pivotal neuroendocrine tissues known to play an essential role in controlling the body's endocrine system, survival, and reproduction. In addition, most currentvitroculture models for neuroendocrine tissues fail to properly reflect their complex multicellular structure. In this context, we developed a novel microscale chip platform, termed the 'hypothalamic-pituitary (HP) axis-on-a-chip,' which integrates various cellular components of the hypothalamus and pituitary gland with biomaterials such as collagen and hyaluronic acid. We used non-toxic blood coagulation factors (fibrinogen and thrombin) as natural cross-linking agents to increase the mechanical strength of biomaterials without showing residual toxicity to overcome drawbacks of conventional chemical cross-linking agents. Furthermore, we identified and verified SERPINB2 as a reliable neuroendocrine toxic marker, with its expression significantly increased in both hypothalamus and pituitary gland cells following exposure to various types of toxins. Next, we introduced SERPINB2-fluorescence reporter system into loaded hypothalamic cells and pituitary gland cells within each chamber of the HP axis on a chip, respectively. By incorporating this SERPINB2 detection system into the loaded hypothalamic and pituitary gland cells within our chip platform, Our HP axis-on-chip platform can better mimic reciprocal neuroendocrine crosstalk between the hypothalamus and the pituitary gland in the brain microenvironments with improved efficiency in evaluating neuroendocrine toxicities of certain drug candidates.

Brain Stiffness Follows Cuprizone-Induced Variations in Local Myelin Content

Brain maturation and neurological diseases are intricately linked to microstructural changes that inherently affect the brain's mechanical behavior. Animal models are frequently used to explore relative brain stiffness changes as a function of underlying microstructure. Here, we are using the cuprizone mouse model to study indentation-derived stiffness changes resulting from acute and chronic demyelination during a 15-week observation period. We focus on the corpus callosum, cingulum, and cortex which undergo different degrees of de- and remyelination and, therefore, result in region-specific stiffness changes. Mean stiffness of the corpus callosum starts at 1.1 ± 0.3 kPa in untreated mice, then cuprizone treatment causes stiffness to drop to 0.6 ± 0.1 kPa by week 3, temporarily increase to 0.9 ± 0.3 kPa by week 6, and ultimately stabilize around 0.7 ± 0.1 kPa by week 9 for the rest of the observation period. The cingulum starts at 3.2 ± 0.9 kPa, then drops to 1.6 ± 0.4 kPa by week 3, and then gradually stabilizes around 1.4 ± 0.3 kPa by week 9. Cortical stiffness exhibits less stiffness variations overall; it starts at 4.2 ± 1.3 kPa, drops to 2.4 ± 0.6 kPa by week 3, and stabilizes around 2.7 ± 0.9 kPa by week 6. We also assess the impact of tissue fixation on indentation-based mechanical tissue characterization. On the one hand, fixation drastically increases untreated mean tissue stiffness by a factor of 3.3 for the corpus callosum, 2.9 for the cingulum, and 3.6 for the cortex; on the other hand, fixation influences interregional stiffness ratios during demyelination, thus suggesting that fixation affects individual brain tissues differently. Lastly, we determine the spatial correlation between stiffness measurements and myelin density and observe a region-specific proportionality between myelin content and tissue stiffness. STATEMENT OF SIGNIFICANCE: Despite extensive work, the relationship between microstructure and mechanical behavior in the brain remains mostly unknown. Additionally, the existing variation of measurement results reported in literature requires in depth investigation of the impact of individual cell and protein populations on tissue stiffness and interregional stiffness ratios. Here, we used microindentation measurements to show that brain stiffness changes with myelin density in the cuprizone-based demyelination mouse model. Moreover, we explored the impact of tissue fixation prior to mechanical characterization because of conflicting results reported in literature. We observe that fixation has a distinctly different impact on our three regions of interest, thus causing region-specific tissue stiffening and, more importantly, changing interregional stiffness ratios.

The effect of the properties of cell nucleus and underlying substrate on the response of finite element models of astrocytes undergoing mechanical stimulations

Astrocyte cells play a critical role in the mechanical behaviour of the brain tissue; hence understanding the properties of Astrocytes is a big step toward understanding brain diseases and abnormalities. Conventionally, atomic force microscopy (AFM) has been used as one of the most powerful tools to characterize the mechanical properties of cells. However, due to the complexities of experimental work and the complex behaviour of living cells, the finite element method (FEM) is commonly used to estimate the cells' response to mechanical stimulations. In this study, we developed a finite element model of the Astrocyte cells to investigate the effect of two key parameters that could affect the response of the cell to mechanical loading; the properties of the underlying substrate and the nucleus. In this regard, the cells were placed on two different substrates in terms of thickness and stiffness (gel and glass) with varying properties of the nucleus. The main achievement of this study was to develop an insight to investigate the response of the Astrocytes to mechanical loading for future studies, both experimentally and computationally.

Automated model discovery for muscle using constitutive recurrent neural networks

The stiffness of soft biological tissues not only depends on the applied deformation, but also on the deformation rate. To model this type of behavior, traditional approaches select a specific time-dependent constitutive model and fit its parameters to experimental data. Instead, a new trend now suggests a machine-learning based approach that simultaneously discovers both the best model and best parameters to explain given data. Recent studies have shown that feed-forward constitutive neural networks can robustly discover constitutive models and parameters for hyperelastic materials. However, feed-forward architectures fail to capture the history dependence of viscoelastic soft tissues. Here we combine a feed-forward constitutive neural network for the hyperelastic response and a recurrent neural network for the viscous response inspired by the theory of quasi-linear viscoelasticity. Our novel rheologically-informed network architecture discovers the time-independent initial stress using the feed-forward network and the time-dependent relaxation using the recurrent network. We train and test our combined network using unconfined compression relaxation experiments of passive skeletal muscle and compare our discovered model to a neo Hookean standard linear solid, to an advanced mechanics-based model, and to a vanilla recurrent neural network with no mechanics knowledge. We demonstrate that, for limited experimental data, our new constitutive recurrent neural network discovers models and parameters that satisfy basic physical principles and generalize well to unseen data. We discover a Mooney-Rivlin type two-term initial stored energy function that is linear in the first invariant I1 and quadratic in the second invariant I2 with stiffness parameters of 0.60 kPa and 0.55 kPa. We also discover a Prony-series type relaxation function with time constants of 0.362s, 2.54s, and 52.0s with coefficients of 0.89, 0.05, and 0.03. Our newly discovered model outperforms both the neo Hookean standard linear solid and the vanilla recurrent neural network in terms of prediction accuracy on unseen data. Our results suggest that constitutive recurrent neural networks can autonomously discover both model and parameters that best explain experimental data of soft viscoelastic tissues. Our source code, data, and examples are available at https://github.com/LivingMatterLab.

Amyloid Beta Peptides Lead to Mast Cell Activation in a Novel 3D Hydrogel Model

Alzheimer's disease (AD) is a prevalent neurodegenerative disease and the world's primary cause of dementia among the elderly population. The aggregation of toxic amyloid-beta (Aβ) is one of the main pathological hallmarks of the AD brain. Recently, neuroinflammation has been recognized as one of the major features of AD, which involves a network of interactions between immune cells. The mast cell (MC) is an innate immune cell type known to serve as a first responder to pathological changes and crosstalk with microglia and neurons. Although an increased number of mast cells were found near the sites of Aβ deposition, how mast cells are activated in AD is not clear. We developed a 3D culture system to culture MCs and investigated the activation of MCs by Aβ peptides. Because collagen I is the major component of extracellular matrix (ECM) in the brain, we encapsulated human LADR MCs in gels formed by collagen I. We found that 3D-cultured MCs survived and proliferated at the same level as MCs in suspension. Additionally, they can be induced to secrete inflammatory cytokines as well as MC proteases tryptase and chymase by typical MC activators interleukin 33 (IL-33) and IgE/anti-IgE. Culturing with peptides Aβ1-42, Aβ1-40, and Aβ25-35 caused MCs to secrete inflammatory mediators, with Aβ1-42 inducing the maximum level of activation. These data indicate that MCs respond to amyloid deposition to elicit inflammatory responses and demonstrate the validity of collagen gel as a model system to investigate MCs in a 3D environment to understand neuroinflammation in AD.

Biochemical Pathways of Cellular Mechanosensing/Mechanotransduction and Their Role in Neurodegenerative Diseases Pathogenesis

In this review, we shed light on recent advances regarding the characterization of biochemical pathways of cellular mechanosensing and mechanotransduction with particular attention to their role in neurodegenerative disease pathogenesis. While the mechanistic components of these pathways are mostly uncovered today, the crosstalk between mechanical forces and soluble intracellular signaling is still not fully elucidated. Here, we recapitulate the general concepts of mechanobiology and the mechanisms that govern the mechanosensing and mechanotransduction processes, and we examine the crosstalk between mechanical stimuli and intracellular biochemical response, highlighting their effect on cellular organelles' homeostasis and dysfunction. In particular, we discuss the current knowledge about the translation of mechanosignaling into biochemical signaling, focusing on those diseases that encompass metabolic accumulation of mutant proteins and have as primary characteristics the formation of pathological intracellular aggregates, such as Alzheimer's Disease, Huntington's Disease, Amyotrophic Lateral Sclerosis and Parkinson's Disease. Overall, recent findings elucidate how mechanosensing and mechanotransduction pathways may be crucial to understand the pathogenic mechanisms underlying neurodegenerative diseases and emphasize the importance of these pathways for identifying potential therapeutic targets.

Changes in Brain Neuroimmunology Following Injury and Disease

The nervous and immune systems are intimately related in the brain and in the periphery, where changes to one affect the other and vice-versa. Immune cells are responsible for sculpting and pruning neuronal synapses, and play key roles in neuro-development and neurological disease pathology. The immune composition of the brain is tightly regulated from the periphery through the blood-brain barrier (BBB), whose maintenance is driven to a significant extent by extracellular matrix (ECM) components. After a brain insult, the BBB can become disrupted and the composition of the ECM can change. These changes, and the resulting immune infiltration, can have detrimental effects on neurophysiology and are the hallmarks of several diseases. In this review, we discuss some processes that may occur after insult, and potential consequences to brain neuroimmunology and disease progression. We then highlight future research directions and opportunities for further tool development to probe the neuro-immune interface.

Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time-dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Alcohol-Induced Alterations in the Vascular Basement Membrane in the Substantia Nigra of the Adult Human Brain

The blood–brain barrier (BBB) represents a highly specialized interface that acts as the first line of defense against toxins. Herein, we investigated the structural and ultrastructural changes in the basement membrane (BM), which is responsible for maintaining the integrity of the BBB, in the context of chronic alcoholism. Human post-mortem tissues from the Substantia Nigra (SN) region were obtained from 44 individuals, then grouped into controls, age-matched alcoholics, and non-age-matched alcoholics and assessed using light and electron microscopy. We found significantly less CD31+ vessels in alcoholic groups compared to controls in both gray and white matter samples. Alcoholics showed increased expression levels of collagen-IV, laminin-111, and fibronectin, which were coupled with a loss of BM integrity in comparison with controls. The BM of the gray matter was found to be more disintegrated than the white matter in alcoholics, as demonstrated by the expression of both collagen-IV and laminin-111, thereby indicating a breakdown in the BM’s structural composition. Furthermore, we observed that the expression of fibronectin was upregulated in the BM of the white matter vasculature in both alcoholic groups compared to controls. Taken together, our findings highlight some sort of aggregation or clumping of BM proteins that occurs in response to chronic alcohol consumption.

Morphological Phenotyping of Organotropic Brain- and Bone-Seeking Triple Negative Metastatic Breast Tumor Cells

Triple negative breast cancer (TNBC) follows a non-random pattern of metastasis to the bone and brain tissue. Prior work has found that brain-seeking breast tumor cells display altered proteomic profiles, leading to alterations in pathways related to cell signaling, cell cycle, metabolism, and extracellular matrix remodeling. Given the unique microenvironmental characteristics of brain and bone tissue, we hypothesized that brain- or bone-seeking TNBC cells may have altered morphologic or migratory phenotypes from each other, or from the parental TNBC cells, as a function of the biochemical or mechanical microenvironment. In this study, we utilized TNBC cells (MDA-MB-231) that were conditioned to metastasize solely to brain (MDA-BR) or bone (MDA-BO) tissue. We quantified characteristics such as cell morphology, migration, and stiffness in response to cues that partially mimic their final metastatic niche. We have shown that MDA-BO cells have a distinct protrusive morphology not found in MDA-P or MDA-BR. Further, MDA-BO cells migrate over a larger area when on a collagen I (abundant in bone tissue) substrate when compared to fibronectin (abundant in brain tissue). However, migration in highly confined environments was similar across the cell types. Modest differences were found in the stiffness of MDA-BR and MDA-BO cells plated on collagen I vs. fibronectin-coated surfaces. Lastly, MDA-BO cells were found to have larger focal adhesion area and density in comparison with the other two cell types. These results initiate a quantitative profile of mechanobiological phenotypes in TNBC, with future impacts aiming to help predict metastatic propensities to organ-specific sites in a clinical setting.