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Monday, August 12th, 2019
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| 10:41a |
Tissue model reveals role of blood-brain barrier in Alzheimer’s Beta-amyloid plaques, the protein aggregates that form in the brains of Alzheimer’s patients, disrupt many brain functions and can kill neurons. They can also damage the blood-brain barrier — the normally tight border that prevents harmful molecules in the bloodstream from entering the brain.
MIT engineers have now developed a tissue model that mimics beta-amyloid’s effects on the blood-brain barrier, and used it to show that this damage can lead molecules such as thrombin, a clotting factor normally found in the bloodstream, to enter the brain and cause additional damage to Alzheimer’s neurons.
“We were able to show clearly in this model that the amyloid-beta secreted by Alzheimer’s disease cells can actually impair barrier function, and once that is impaired, factors are secreted into the brain tissue that can have adverse effects on neuron health,” says Roger Kamm, the Cecil and Ida Green Distinguished Professor of Mechanical and Biological Engineering at MIT.
The researchers also used the tissue model to show that a drug that restores the blood-brain barrier can slow down the cell death seen in Alzheimer’s neurons.
Kamm and Rudolph Tanzi, a professor of neurology at Harvard Medical School and Massachusetts General Hospital, are the senior authors of the study, which appears in the XX issue of the journal Advanced Science. MIT postdoc Yoojin Shin is the paper’s lead author.
Barrier breakdown
The blood vessel cells that make up the blood-brain barrier have many specialized proteins that help them to form tight junctions — cellular structures that act as a strong seal between cells.
Alzheimer’s patients often experience damage to brain blood vessels caused by beta-amyloid proteins, an effect known as cerebral amyloid angiopathy (CAA). It is believed that this damage allows harmful molecules to get into the brain more easily. Kamm decided to study this phenomenon, and its role in Alzheimer’s, by modeling brain and blood vessel tissue on a microfluidic chip.
“What we were trying to do from the start was generate a model that we could use to understand the interactions between Alzheimer’s disease neurons and the brain vasculature,” Kamm says. “Given the fact that there’s been so little success in developing therapeutics that are effective against Alzheimer’s, there has been increased attention paid to CAA over the last couple of years.”
His lab began working on this project several years ago, along with researchers at MGH who had engineered neurons to produce large amounts of beta-amyloid proteins, just like the brain cells of Alzheimer’s patients.
Led by Shin, the researchers devised a way to grow these cells in a microfluidic channel, where they produce and secrete beta-amyloid protein. On the same chip, in a parallel channel, the researchers grew brain endothelial cells, which are the cells that form the blood-brain barrier. An empty channel separated the two channels while each tissue type developed.
After 10 days of cell growth, the researchers added collagen to the central channel separating the two tissue types, which allowed molecules to diffuse from one channel to the other. They found that within three to six days, beta-amyloid proteins secreted by the neurons began to accumulate in the endothelial tissue, which led the cells to become leakier. These cells also showed a decline in proteins that form tight junctions, and an increase in enzymes that break down the extracellular matrix that normally surrounds and supports blood vessels.
As a result of this breakdown in the blood-brain barrier, thrombin was able to pass from blood flowing through the leaky vessels into the Alzheimer’s neurons. Excessive levels of thrombin can harm neurons and lead to cell death.
“We were able to demonstrate this bidirectional signaling between cell types and really solidify things that had been seen previously in animal experiments, but reproduce them in a model system that we can control with much more detail and better fidelity,” Kamm says.
Plugging the leaks
The researchers then decided to test two drugs that have previously been shown to solidify the blood-brain barrier in simpler models of endothelial tissue. Both of these drugs are FDA-approved to treat other conditions. The researchers found that one of these drugs, etodolac, worked very well, while the other, beclomethasone, had little effect on leakiness in their tissue model.
In tissue treated with etodolac, the blood-brain barrier became tighter, and neurons’ survival rates improved. The MIT and MGH team is now working with a drug discovery consortium to look for other drugs that might be able to restore the blood-brain barrier in Alzheimer’s patients.
“We’re starting to use this platform to screen for drugs that have come out of very simple single cell screens that we now need to validate in a more complex system,” Kamm says. “This approach could offer a new potential form of Alzheimer’s treatment, especially given the fact that so few treatments have been demonstrated to be effective.”
The research was funded by the Cure Alzheimer’s Fund and the JPB Foundation. | | 11:00a |
New type of electrolyte could enhance supercapacitor performance Supercapacitors, electrical devices that store and release energy, need a layer of electrolyte — an electrically conductive material that can be solid, liquid, or somewhere in between. Now, researchers at MIT and several other institutions have developed a novel class of liquids that may open up new possibilities for improving the efficiency and stability of such devices while reducing their flammability.
“This proof-of-concept work represents a new paradigm for electrochemical energy storage,” the researchers say in their paper describing the finding, which appears today in the journal Nature Materials.
For decades, researchers have been aware of a class of materials known as ionic liquids — essentially, liquid salts — but this team has now added to these liquids a compound that is similar to a surfactant, like those used to disperse oil spills. With the addition of this material, the ionic liquids “have very new and strange properties,” including becoming highly viscous, says MIT postdoc Xianwen Mao PhD ’14, the lead author of the paper.
“It’s hard to imagine that this viscous liquid could be used for energy storage,” Mao says, “but what we find is that once we raise the temperature, it can store more energy, and more than many other electrolytes.”
That’s not entirely surprising, he says, since with other ionic liquids, as temperature increases, “the viscosity decreases and the energy-storage capacity increases.” But in this case, although the viscosity stays higher than that of other known electrolytes, the capacity increases very quickly with increasing temperature. That ends up giving the material an overall energy density — a measure of its ability to store electricity in a given volume — that exceeds those of many conventional electrolytes, and with greater stability and safety.
The key to its effectiveness is the way the molecules within the liquid automatically line themselves up, ending up in a layered configuration on the metal electrode surface. The molecules, which have a kind of tail on one end, line up with the heads facing outward toward the electrode or away from it, and the tails all cluster in the middle, forming a kind of sandwich. This is described as a self-assembled nanostructure.
“The reason why it’s behaving so differently” from conventional electrolytes is because of the way the molecules intrinsically assemble themselves into an ordered, layered structure where they come in contact with another material, such as the electrode inside a supercapacitor, says T. Alan Hatton, a professor of chemical engineering at MIT and the paper’s senior author. “It forms a very interesting, sandwich-like, double-layer structure.”
This highly ordered structure helps to prevent a phenomenon called “overscreening” that can occur with other ionic liquids, in which the first layer of ions (electrically charged atoms or molecules) that collect on an electrode surface contains more ions than there are corresponding charges on the surface. This can cause a more scattered distribution of ions, or a thicker ion multilayer, and thus a loss of efficiency in energy storage; “whereas with our case, because of the way everything is structured, charges are concentrated within the surface layer,” Hatton says.
The new class of materials, which the researchers call SAILs, for surface-active ionic liquids, could have a variety of applications for high-temperature energy storage, for example for use in hot environments such as in oil drilling or in chemical plants, according to Mao. “Our electrolyte is very safe at high temperatures, and even performs better,” he says. In contrast, some electrolytes used in lithium-ion batteries are quite flammable.
The material could help to improve performance of supercapacitors, Mao says. Such devices can be used to store electrical charge and are sometimes used to supplement battery systems in electric vehicles to provide an extra boost of power. Using the new material instead of a conventional electrolyte in a supercapacitor could increase its energy density by a factor of four or five, Mao says. Using the new electrolyte, future supercapacitors may even be able to store more energy than batteries, he says, potentially even replacing batteries in applications such as electric vehicles, personal electronics, or grid-level energy storage facilities.
The material could also be useful for a variety of emerging separation processes, Mao says. “A lot of newly developed separation processes require electrical control,” in various chemical processing and refining applications and in carbon dioxide capture, for example, as well as resource recovery from waste streams. These ionic liquids, being highly conductive, could be well-suited to many such applications, he says.
The material they initially developed is just an example of a variety of possible SAIL compounds. “The possibilities are almost unlimited,” Mao says. The team will continue to work on different variations and on optimizing its parameters for particular uses. “It might take a few months or years,” he says, “but working on a new class of materials is very exciting to do. There are many possibilities for further optimization.”
The research team included Paul Brown, Yinying Ren, Agilio Padua, and Margarida Costa Gomes at MIT; Ctirad Cervinka at École Normale Supérieure de Lyon, in France; Gavin Hazell and Julian Eastoe at the University of Bristol, in the U.K.; Hua Li and Rob Atkin at the University of Western Australia; and Isabelle Grillo at the Institut Max-von-Laue-Paul-Langevin in Grenoble, France. The researchers dedicate their paper to the memory of Grillo, who recently passed away.
“It is a very exciting result that surface-active ionic liquids (SAILs) with amphiphilic structures can self-assemble on electrode surfaces and enhance charge storage performance at electrified surfaces,” says Yi Cui, a professor of materials science and engineering at Stanford University, who was not associated with this research. “The authors have studied and understood the mechanism. The work here might have a great impact on the design of high energy density supercapacitors, and could also help improve battery performance,” he says.
Nicholas Abbott, a University Professor of Chemistry at Cornell University, who also was not involved in this work, says “The paper describes a very clever advance in interfacial charge storage, elegantly demonstrating how knowledge of molecular self-assembly at interfaces can be leveraged to address a contemporary technological challenge.”
The work was supported by the MIT Energy Initiative, an MIT Skoltech fellowship, and the Czech Science Foundation. | | 1:00p |
Finding the brain’s compass The world is constantly bombarding our senses with information, but the ways in which our brain extracts meaning from this information remains elusive. How do neurons transform raw visual input into a mental representation of an object — like a chair or a dog?
In work published in Nature Neuroscience, MIT neuroscientists have identified a brain circuit in mice that distills “high-dimensional” complex information about the environment into a simple abstract object in the brain.
“There are no degree markings in the external world; our current head direction has to be extracted, computed, and estimated by the brain,” explains Ila Fiete, an associate member of the McGovern Institute and senior author of the paper. “The approaches we used allowed us to demonstrate the emergence of a low-dimensional concept, essentially an abstract compass in the brain.”
This abstract compass, according to the researchers, is a one-dimensional ring that represents the current direction of the head relative to the external world.
Schooling fish
Trying to show that a data cloud has a simple shape, like a ring, is a bit like watching a school of fish. By tracking one or two sardines, you might not see a pattern. But if you could map all of the sardines, and transform the noisy dataset into points representing the positions of the whole school of sardines over time, and where each fish is relative to its neighbors, a pattern would emerge. This model would reveal a ring shape, a simple shape formed by the activity of hundreds of individual fish.
Fiete, who is also an associate professor in MIT’s Department of Brain and Cognitive Sciences, used a similar approach, called topological modeling, to transform the activity of large populations of noisy neurons into a data cloud in the shape of a ring.
Simple and persistent ring
Previous work in fly brains revealed a physical ellipsoid ring of neurons representing changes in the direction of the fly’s head, and researchers suspected that such a system might also exist in mammals.
In this new mouse study, Fiete and her colleagues measured hours of neural activity from scores of neurons in the anterodorsal thalamic nucleus (ADN) — a region believed to play a role in spatial navigation — as the animals moved freely around their environment. They mapped how the neurons in the ADN circuit fired as the animal’s head changed direction.
Together, these data points formed a cloud in the shape of a simple and persistent ring.
“This tells us a lot about how neural networks are organized in the brain,” explains Edvard Moser, director of the Kavli Institute of Systems Neuroscience in Norway, who was not involved in the study. “Past data have indirectly pointed towards such a ring-like organization, but only now has it been possible, with the right cell numbers and methods, to demonstrate it convincingly,” says Moser.
Their method for characterizing the shape of the data cloud allowed Fiete and colleagues to determine which variable the circuit was devoted to representing, and to decode this variable over time, using only the neural responses.
“The animal’s doing really complicated stuff,” explains Fiete, “but this circuit is devoted to integrating the animal’s speed along a one-dimensional compass that encodes head direction. Without a manifold approach, which captures the whole state space, you wouldn’t know that this circuit of thousands of neurons is encoding only this one aspect of the complex behavior, and not encoding any other variables at the same time.”
Even during sleep, when the circuit is not being bombarded with external information, this circuit robustly traces out the same one-dimensional ring, as if dreaming of past head-direction trajectories.
Further analysis revealed that the ring acts an attractor. If neurons stray off trajectory, they are drawn back to it, quickly correcting the system. This attractor property of the ring means that the representation of head direction in abstract space is reliably stable over time, a key requirement if we are to understand and maintain a stable sense of where our head is relative to the world around us.
“In the absence of this ring,” Fiete explains, “we would be lost in the world.”
Shaping the future
Fiete’s work provides a first glimpse into how complex sensory information is distilled into a simple concept in the mind, and how that representation autonomously corrects errors, making it exquisitely stable.
But the implications of this study go beyond coding of head direction.
“Similar organization is probably present for other cognitive functions, so the paper is likely to inspire numerous new studies,” says Moser.
Fiete sees these analyses and related studies carried out by colleagues at the Norwegian University of Science and Technology, Princeton University, the Weitzman Institute, and elsewhere as fundamental to the future of neural decoding studies.
With this approach, she explains, it is possible to extract abstract representations of the mind from the brain, potentially even thoughts and dreams.
“We’ve found that the brain deconstructs and represents complex things in the world with simple shapes,” explains Fiete. “Manifold-level analysis can help us to find those shapes, and they almost certainly exist beyond head-direction circuits.” |
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