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Discovering the Brain’s Nightly “Rinse Cycle”

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Getting plenty of deep, restful sleep is essential for our physical and mental health. Now comes word of yet another way that sleep is good for us: it triggers rhythmic waves of blood and cerebrospinal fluid (CSF) that appear to function much like a washing machine’s rinse cycle, which may help to clear the brain of toxic waste on a regular basis.

The video above uses functional magnetic resonance imaging (fMRI) to take you inside a person’s brain to see this newly discovered rinse cycle in action. First, you see a wave of blood flow (red, yellow) that’s closely tied to an underlying slow-wave of electrical activity (not visible). As the blood recedes, CSF (blue) increases and then drops back again. Then, the cycle—lasting about 20 seconds—starts over again.

The findings, published recently in the journal Science, are the first to suggest that the brain’s well-known ebb and flow of blood and electrical activity during sleep may also trigger cleansing waves of blood and CSF. While the experiments were conducted in healthy adults, further study of this phenomenon may help explain why poor sleep or loss of sleep has previously been associated with the spread of toxic proteins and worsening memory loss in people with Alzheimer’s disease.

In the new study, Laura Lewis, Boston University, MA, and her colleagues at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston. recorded the electrical activity and took fMRI images of the brains of 13 young, healthy adults as they slept. The NIH-funded team also built a computer model to learn more about the fluid dynamics of what goes on in the brain during sleep. And, as it turns out, their sophisticated model predicted exactly what they observed in the brains of living humans: slow waves of electrical activity followed by alternating waves of blood and CSF.

Lewis says her team is now working to come up with even better ways to capture CSF flow in the brain during sleep. Currently, people who volunteer for such experiments have to be able to fall asleep while wearing an electroencephalogram (EEG) cap inside of a noisy MRI machine—no easy feat. The researchers are also recruiting older adults to begin exploring how age-related changes in brain activity during sleep may affect the associated fluid dynamics.

Reference:

[1] Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep. Fultz NE, Bonmassar G, Setsompop K, Stickgold RA, Rosen BR, Polimeni JR, Lewis LD. Science. 2019 Nov 1;366(6465):628-631.

Links:

Sleep and Memory (National Institute of Mental Health/NIH)

Sleep Deprivation and Deficiency (National Heart, Lung, and Blood Institute/NIH)

Alzheimer’s Disease and Related Dementias (National Institute on Aging/NIH)

NIH Support: National Institute of Mental Health; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke


Battling Malaria at the Atomic Level

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Cryo-EM Image of P. falciparum Protein
Credit: Columbia University Irving Medical Center, New York

Tropical medicine has its share of wily microbes. Among the most clever is the mosquito-borne protozoan Plasmodium falciparum, which is the cause of the most common—and most lethal—form of malaria. For decades, doctors have used antimalarial drugs against P. falciparum. But just when malaria appeared to be well on its way to eradication, this parasitic protozoan mutated in ways that has enabled it to resist frontline antimalarial drugs. This resistance is a major reason that malaria, one of the world’s oldest diseases, still claims the lives of about 400,000 people each year [1].

This is a situation with which I have personal experience. Thirty years ago before traveling to Nigeria, I followed directions and took chloroquine to prevent malaria. But the resistance to the drug was already widespread, and I came down with malaria anyway. Fortunately, the parasite that a mosquito delivered to me was sensitive to another drug called Fansidar, which acts through another mechanism. I was pretty sick for a few days, but recovered without lasting consequences.

While new drugs are being developed to thwart P. falciparum, some researchers are busy developing tools to predict what mutations are likely to occur next in the parasite’s genome. And that’s what is so exciting about the image above. It presents the unprecedented, 3D atomic-resolution structure of a protein made by P. falciparum that’s been a major source of its resistance: the chloroquine-resistance transporter protein, or PfCRT.

In this cropped density map, you see part of the protein’s biochemical structure. The colorized area displays the long, winding chain of amino acids within the protein as helices in shades of green, blue and gold. These helices enclose a central cavity essential for the function of the protein, whose electrostatic properties are shown here as negative (red), positive (blue), and neutral (white). All this structural information was captured using cryo-electron microscopy (cryo-EM). The technique involves flash-freezing molecules in liquid nitrogen and bombarding them with electrons to capture their images with a special camera.

This groundbreaking work, published recently in Nature, comes from an NIH-supported multidisciplinary research team, led by David Fidock, Matthias Quick, and Filippo Mancia, Columbia University Irving Medical Center, New York [2]. It marks a major feat for structural biology, because PfCRT is on the small side for standard cryo-EM and, as Mancia discovered, the protein is almost featureless.

These two strikes made Mancia and colleagues wonder at first whether they would swing and miss at their attempt to image the protein. With the help of coauthor Anthony Kossiakoff, a researcher at the University of Chicago, the team complexed PfCRT to a bulkier antibody fragment. That doubled the size of their subject, and the fragment helped to draw out PfCRT’s hidden features. One year and a lot of hard work later, they got their homerun.

PfCRT is a transport protein embedded in the surface membrane of what passes for the gut of P. falciparum. Because the gene encoding it is highly mutable, the PfCRT protein modified its structure many years ago, enabling it to pump out and render ineffective several drugs in a major class of antimalarials called 4-aminoquinolines. That includes chloroquine.

Now, with the atomic structure in hand, researchers can map the locations of existing mutations and study how they work. This information will also allow them to model which regions of the protein to be on the lookout for the next adaptive mutations. The hope is this work will help to prolong the effectiveness of today’s antimalarial drugs.

For example, the drug piperaquine, a 4-aminoquinoline agent, is now used in combination with another antimalarial. The combination has proved quite effective. But recent reports show that P. falciparum has acquired resistance to piperaquine, driven by mutations in PfCRT that are spreading rapidly across Southeast Asia [3].

Interestingly, the researchers say they have already pinpointed single mutations that could confer piperaquine resistance to parasites from South America. They’ve also located where new mutations are likely to occur to compromise the drug’s action in Africa, where most malarial infections and deaths occur. So, this atomic structure is already being put to good use.

Researchers also hope that this model will allow drug designers to make structural adjustments to old, less effective malarial drugs and perhaps restore them to their former potency. Perhaps this could even be done by modifying chloroquine, introduced in the 1940s as the first effective antimalarial. It was used worldwide but was largely shelved a few decades later due to resistance—as I experienced three decades ago.

Malaria remains a constant health threat for millions of people living in subtropical areas of the world. Wouldn’t it be great to restore chloroquine to the status of a frontline antimalarial? The drug is inexpensive, taken orally, and safe. Through the power of science, its return is no longer out of the question.

References:

[1] World malaria report 2019. World Health Organization, December 4, 2019

[2] Structure and drug resistance of the Plasmodium falciparum transporter PfCRT. Kim J, Tan YZ, Wicht KJ, Erramilli SK, Dhingra SK, Okombo J, Vendome J, Hagenah LM, Giacometti SI, Warren AL, Nosol K, Roepe PD, Potter CS, Carragher B, Kossiakoff AA, Quick M, Fidock DA, Mancia F. Nature. 2019 Dec;576(7786):315-320.

[3] Determinants of dihydroartemisinin-piperaquine treatment failure in Plasmodium falciparum malaria in Cambodia, Thailand, and Vietnam: a prospective clinical, pharmacological, and genetic study. van der Pluijm RW, Imwong M, Chau NH, Hoa NT, et. al. Lancet Infect Dis. 2019 Sep;19(9):952-961.

Links:

Malaria (National Institute of Allergy and Infectious Diseases/NIH)

Fidock Lab (Columbia University Irving Medical Center, New York)

Video: David Fidock on antimalarial drug resistance (BioMedCentral/YouTube)

Kossiakoff Lab (University of Chicago)

Mancia Lab (Columbia University Irving Medical Center)

Matthias Quick (Columbia University Irving Medical Center)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute


Artificial Intelligence Speeds Brain Tumor Diagnosis

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Real time diagnostics in the operating room
Caption: Artificial intelligence speeds diagnosis of brain tumors. Top, doctor reviews digitized tumor specimen in operating room; left, the AI program predicts diagnosis; right, surgeons review results in near real-time.
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor

Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.

This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.

Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.

As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.

The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.

Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.

To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.

In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.

The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.

Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.

Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.

Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.

The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.

Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.

Reference:

[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.

Links:

Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)

Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)

Todd Hollon (University of Michigan, Ann Arbor)

NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering


3D Neuroscience at the Speed of Life

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This fluorescent worm makes for much more than a mesmerizing video. It showcases a significant technological leap forward in our ability to capture in real time the firing of individual neurons in a living, freely moving animal.

As this Caenorhabditis elegans worm undulates, 113 neurons throughout its brain and body (green/yellow spots) get brighter and darker as each neuron activates and deactivates. In fact, about halfway through the video, you can see streaks tracking the positions of individual neurons (blue/purple-colored lines) from one frame to the next. Until now, it would have been technologically impossible to capture this “speed of life” with such clarity.

With funding from the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, Elizabeth Hillman at Columbia University’s Zuckerman Institute, New York, has pioneered the pairing of a 3D live-imaging microscope with an ultra-fast camera. This pairing, showcased above, is a technique called Swept Confocally Aligned Planar Excitation (SCAPE) microscopy.

Since first demonstrating SCAPE in February 2015 [1], Hillman and her team have worked hard to improve, refine, and expand the approach. Recently, they used SCAPE 1.0 to image how proprioceptive neurons in fruit-fly larvae sense body position while crawling. Now, as described in Nature Methods, they introduce SCAPE “2.0,” with boosted resolution and a much faster camera—enabling 3D imaging at speeds hundreds of times faster than conventional microscopes [2]. To track a very wiggly worm, the researchers image their target 25 times a second!

As with the first-generation SCAPE, version 2.0 uses a scanning mirror to sweep a slanted sheet of light across a sample. This same mirror redirects light coming from the illuminated plane to focus onto a stationary high-speed camera. The approach lets SCAPE grab 3D imaging at very high speeds, while also causing very little photobleaching compared to conventional point-scanning microscopes, reducing sample damage that often occurs during time-lapse microscopy.

Like SCAPE 1.0, since only a single, stationary objective lens is used, the upgraded 2.0 system doesn’t need to hold, move, or disturb a sample during imaging. This flexibility enables scientists to use SCAPE in a wide range of experiments where they can present stimuli or probe an animal’s behavior—all while imaging how the underlying cells drive and depict those behaviors.

The SCAPE 2.0 paper shows the system’s biological versatility by also recording the beating heart of a zebrafish embryo at record-breaking speeds. In addition, SCAPE 2.0 can rapidly image large fixed, cleared, and expanded tissues such as the retina, brain, and spinal cord—enabling tracing of the shape and connectivity of cellular circuits. Hillman and her team are dedicated to exporting their technology; they provide guidance and a parts list for SCAPE 2.0 so that researchers can build their own version using inexpensive off-the-shelf parts.

Watching worms wriggling around may remind us of middle-school science class. But to neuroscientists, these images represent progress toward understanding the nervous system in action, literally at the speed of life!

References:

[1] . Swept confocally-aligned planar excitation (SCAPE) microscopy for high speed volumetric imaging of behaving organisms. Bouchard MB, Voleti V, Mendes CS, Lacefield C, et al Nature Photonics. 2015;9(2):113-119.

[2] Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Voleti V, Patel KB, Li W, Campos CP, et al. Nat Methods. 2019 Sept 27;16:1054–1062.

Links:

Using Research Organisms to Study Health and Disease (National Institute of General Medical Sciences/NIH)

The Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Hillman Lab (Columbia University, New York)

NIH Support: National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute


Multiplex Rainbow Technology Offers New View of the Brain

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Proteins imaged with this new approach
Caption: Confocal LNA-PRISM imaging of neuronal synapses. Conventional images of cell nuclei and two proteins (top row, three images on the left), along with 11 PRISM images of proteins and one composite, multiplexed image (bottom row, right). Credit: Adapted from Guo SM, Nature Communications, 2019

The NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative is revolutionizing our understanding of how the brain works through its creation of new imaging tools. One of the latest advances—used to produce this rainbow of images—makes it possible to view dozens of proteins in rapid succession in a single tissue sample containing thousands of neural connections, or synapses.

Apart from their colors, most of these images look nearly identical at first glance. But, upon closer inspection, you’ll see some subtle differences among them in both intensity and pattern. That’s because the images capture different proteins within the complex network of synapses—and those proteins may be present in that network in different amounts and locations. Such findings may shed light on key differences among synapses, as well as provide new clues into the roles that synaptic proteins may play in schizophrenia and various other neurological disorders.

Synapses contain hundreds of proteins that regulate the release of chemicals called neurotransmitters, which allow neurons to communicate. Each synaptic protein has its own specific job in the process. But there have been longstanding technical difficulties in observing synaptic proteins at work. Conventional fluorescence microscopy can visualize at most four proteins in a synapse.

As described in Nature Communications [1], researchers led by Mark Bathe, Massachusetts Institute of Technology (MIT), Cambridge, and Jeffrey Cottrell, Broad Institute of MIT and Harvard, Cambridge, have just upped this number considerably while delivering high quality images. They did it by adapting an existing imaging method called DNA PAINT [2]. The researchers call their adapted method PRISM. It is short for: Probe-based Imaging for Sequential Multiplexing.

Here’s how it works: First, researchers label proteins or other molecules of interest using antibodies that recognize those proteins. Those antibodies include a unique DNA probe that helps with the next important step: making the proteins visible under a microscope.

To do it, they deliver short snippets of complementary fluorescent DNA, which bind the DNA-antibody probes. While each protein of interest is imaged separately, researchers can easily wash the probes from a sample to allow a series of images to be generated, each capturing a different protein of interest.

In the original DNA PAINT, the DNA strands bind and unbind periodically to create a blinking fluorescence that can be captured using super-resolution microscopy. But that makes the process slow, requiring about half an hour for each protein.

To speed things up with PRISM, Bathe and his colleagues altered the fluorescent DNA probes. They used synthetic DNA that’s specially designed to bind more tightly or “lock” to the DNA-antibody. This gives a much brighter signal without the blinking effect. As a result, the imaging can be done faster, though at slightly lower resolution.

Though the team now captures images of 12 proteins within a sample in about an hour, this is just a start. As more DNA-antibody probes are developed for synaptic proteins, the team can readily ramp up this number to 30 protein targets.

Thanks to the BRAIN Initiative, researchers now possess a powerful new tool to study neurons. PRISM will help them learn more mechanistically about the inner workings of synapses and how they contribute to a range of neurological conditions.

References:

[1] Multiplexed and high-throughput neuronal fluorescence imaging with diffusible probes. Guo SM, Veneziano R, Gordonov S, Li L, Danielson E, Perez de Arce K, Park D, Kulesa AB, Wamhoff EC, Blainey PC, Boyden ES, Cottrell JR, Bathe M. Nat Commun. 2019 Sep 26;10(1):4377.

[2] Super-resolution microscopy with DNA-PAINT. Schnitzbauer J, Strauss MT, Schlichthaerle T, Schueder F, Jungmann R. Nat Protoc. 2017 Jun;12(6):1198-1228.

Links:

Schizophrenia (National Institute of Mental Health)

Mark Bathe (Massachusetts Institute of Technology, Cambridge)

Jeffrey Cottrell (Broad Institute of MIT and Harvard, Cambridge)

Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

NIH Support: National Institute of Mental Health; National Human Genome Research Institute; National Institute of Neurological Disorders and Stroke; National Institute of Environmental Health Sciences


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