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High-resolution imaging with conventional microscopes

Anne Trafton |

MIT researchers have developed a way to make extremely high-resolution images of tissue samples, at a fraction of the cost of other techniques that offer similar resolution.

The new technique relies on expanding tissue before imaging it with a conventional light microscope. Two years ago, the MIT team showed that it was possible to expand tissue volumes 100-fold, resulting in an image resolution of about 60 nanometers. Now, the researchers have shown that expanding the tissue a second time before imaging can boost the resolution to about 25 nanometers.

This level of resolution allows scientists to see, for example, the proteins that cluster together in complex patterns at brain synapses, helping neurons to communicate with each other. It could also help researchers to map neural circuits, says Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT.

“We want to be able to trace the wiring of complete brain circuits,” says Boyden, the study’s senior author. “If you could reconstruct a complete brain circuit, maybe you could make a computational model of how it generates complex phenomena like decisions and emotions. Since you can map out the biomolecules that generate electrical pulses within cells and that exchange chemicals between cells, you could potentially model the dynamics of the brain.”

This approach could also be used to image other phenomena such as the interactions between cancer cells and immune cells, to detect pathogens without expensive equipment, and to map the cell types of the body.

Former MIT postdoc Jae-Byum Chang is the first author of the paper, which appears in the April 17 issue of Nature Methods.

Double expansion

To expand tissue samples, the researchers embed them in a dense, evenly generated gel made of polyacrylate, a very absorbent material that’s also used in diapers. Before the gel is formed, the researchers label the cell proteins they want to image, using antibodies that bind to specific targets. These antibodies bear “barcodes” made of DNA, which in turn are attached to cross-linking molecules that bind to the polymers that make up the expandable gel. The researchers then break down the proteins that normally hold the tissue together, allowing the DNA barcodes to expand away from each other as the gel swells.

These enlarged samples can then be labeled with fluorescent probes that bind the DNA barcodes, and imaged with commercially available confocal microscopes, whose resolution is usually limited to hundreds of nanometers.

Using that approach, the researchers were previously able to achieve a resolution of about 60 nanometers. However, “individual biomolecules are much smaller than that, say 5 nanometers or even smaller,” Boyden says. “The original versions of expansion microscopy were useful for many scientific questions but couldn’t equal the performance of the highest-resolution imaging methods such as electron microscopy.”

In their original expansion microscopy study, the researchers found that they could expand the tissue more than 100-fold in volume by reducing the number of cross-linking molecules that hold the polymer in an orderly pattern. However, this made the tissue unstable.

“If you reduce the cross-linker density, the polymers no longer retain their organization during the expansion process,” says Boyden, who is a member of MIT’s Media Lab and McGovern Institute for Brain Research. “You lose the information.”

Instead, in their latest study, the researchers modified their technique so that after the first tissue expansion, they can create a new gel that swells the tissue a second time — an approach they call “iterative expansion.”

Mapping circuits

Using iterative expansion, the researchers were able to image tissues with a resolution of about 25 nanometers, which is similar to that achieved by high-resolution techniques such as stochastic optical reconstruction microscopy (STORM). However, expansion microscopy is much cheaper and simpler to perform because no specialized equipment or chemicals are required, Boyden says. The method is also much faster and thus compatible with large-scale, 3-D imaging.

The resolution of expansion microscopy does not yet match that of scanning electron microscopy (about 5 nanometers) or transmission electron microscopy (about 1 nanometer). However, electron microscopes are very expensive and not widely available, and with those microscopes, it is difficult for researchers to label specific proteins.

In the Nature Methods paper, the MIT team used iterative expansion to image synapses — the connections between neurons that allow them to communicate with each other. In their original expansion microscopy study, the researchers were able to image scaffolding proteins, which help to organize the hundreds of other proteins found in synapses. With the new, enhanced resolution, the researchers were also able to see finer-scale structures, such as the location of neurotransmitter receptors located on the surfaces of the “postsynaptic” cells on the receiving side of the synapse.

“My hope is that we can, in the coming years, really start to map out the organization of these scaffolding and signaling proteins at the synapse,” Boyden says.

Combining expansion microscopy with a new tool called temporal multiplexing should help to achieve that, he believes. Currently, only a limited number of colored probes can be used to image different molecules in a tissue sample. With temporal multiplexing, researchers can label one molecule with a fluorescent probe, take an image, and then wash the probe away. This can then be repeated many times, each time using the same colors to label different molecules.

“By combining iterative expansion with temporal multiplexing, we could in principle have essentially infinite-color, nanoscale-resolution imaging over large 3-D volumes,” Boyden says. “Things are getting really exciting now that these different technologies may soon connect with each other.”

The researchers also hope to achieve a third round of expansion, which they believe could, in principle, enable resolution of about 5 nanometers. However, right now the resolution is limited by the size of the antibodies used to label molecules in the cell. These antibodies are about 10 to 20 nanometers long, so to get resolution below that, researchers would need to create smaller tags or expand the proteins away from each other first and then deliver the antibodies after expansion.

This study was funded by the National Institutes of Health Director’s Pioneer Award, the New York Stem Cell Foundation Robertson Award, the HHMI-Simons Faculty Scholars Award, and the Open Philanthropy Project.

Scientists unveil CRISPR-based diagnostic platform

Anne Trafton |

A team of scientists from the Broad Institute of MIT and Harvard, the McGovern Institute for Brain Research at MIT, the Institute for Medical Engineering and Science at MIT, and the Wyss Institute for Biologically Inspired Engineering at Harvard University has adapted a CRISPR protein that targets RNA (rather than DNA), for use as a rapid, inexpensive, highly sensitive diagnostic tool with the potential to transform research and global public health.

In a study published today in Science, Broad Institute members Feng Zhang, Jim Collins, Deb Hung, Aviv Regev, and Pardis Sabeti describe how this RNA-targeting CRISPR enzyme was harnessed as a highly sensitive detector — able to indicate the presence of as little as a single molecule of a target RNA or DNA. Co-first authors Omar Abudayyeh and Jonathan Gootenberg, graduate students at MIT and Harvard, respectively, dubbed the new tool SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing); this technology could one day be used to respond to viral and bacterial outbreaks, monitor antibiotic resistance, and detect cancer.

The scientists demonstrate the method’s versatility on a range of applications, including:

• detecting the presence of Zika virus in patient blood or urine samples within hours;
• distinguishing between the genetic sequences of African and American strains of Zika virus;
• discriminating specific types of bacteria, such as E. coli;
• detecting antibiotic resistance genes;
• identifying cancerous mutations in simulated cell-free DNA fragments; and
• rapidly reading human genetic information, such as risk of heart disease, from a saliva sample.

Because the tool can be designed for use as a paper-based test that does not require refrigeration, the researchers say it is well-suited for fast deployment and widespread use inside and outside of traditional settings — such as at a field hospital during an outbreak, or a rural clinic with limited access to advanced equipment.

“It’s exciting that the Cas13a enzyme, which was originally identified in our collaboration with Eugene Koonin to study the basic biology of bacterial immunity, can be harnessed to achieve such extraordinary sensitivity, which will be powerful for both science and clinical medicine,” says Feng Zhang, core institute member of the Broad Institute, an investigator at the McGovern Institute, and the James and Patricia Poitras ’63 Professor in Neuroscience and associate professor in the departments of Brain and Cognitive Sciences and Biological Engineering at MIT.

In June 2016, Zhang and his colleagues first characterized the RNA-targeting CRISPR enzyme, now called Cas13a (previously known as C2c2), which can be programmed to cleave particular RNA sequences in bacterial cells. Unlike DNA-targeting CRISPR enzymes (such as Cas9 and Cpf1), Cas13a can remain active after cutting its intended RNA target and may continue to cut other nontargeted RNAs in a burst of activity that Zhang lab scientists referred to as “collateral cleavage.” In their paper and patent filing, the team described a wide range of biotechnological applications for the system, including harnessing RNA cleavage and collateral activity for basic research, diagnostics, and therapeutics.

In a paper in Nature in September 2016, Jennifer Doudna, Alexandra East-Seletsky, and their colleagues at the University of California at Berkeley employed the Cas13a collateral cleavage activity for RNA detection. That method required the presence of many millions of molecules, however, and therefore lacked the sensitivity required for many research and clinical applications.

The method reported today is a million-fold more sensitive. This increase was the result of a collaboration between Zhang and his team and Broad Institute member Jim Collins, who had been working on diagnostics for Zika virus.

Working together, the Zhang and Collins teams were able to use a different amplification process, relying on body heat, to boost the levels of DNA or RNA in their test samples. Once the level was increased, the team applied a second amplification step to convert the DNA to RNA, which enabled them to increase the sensitivity of the RNA-targeting CRISPR by a millionfold, all with a tool that can be used in nearly any setting.

“We can now effectively and readily make sensors for any nucleic acid, which is incredibly powerful when you think of diagnostics and research applications,” says Collins, the Termeer Professor of Medical Engineering and Science at MIT and core faculty member at the Wyss Institute. “This tool offers the sensitivity that could detect an extremely small amount of cancer DNA in a patient’s blood sample, for example, which would help researchers understand how cancer mutates over time. For public health, it could help researchers monitor the frequency of antibiotic-resistant bacteria in a population. The scientific possibilities get very exciting very quickly.”

One of the most urgent and obvious applications for this new diagnostic tool would be as a rapid, point-of-care diagnostic for infectious disease outbreaks in resource-poor areas.
“There is great excitement around this system,” says Deb Hung, co-author and co-director of the Broad’s Infectious Disease and Microbiome Program. “There is still much work to be done, but if SHERLOCK can be developed to its full potential it could fundamentally change the diagnosis of common and emerging infectious diseases.”

“One thing that’s especially powerful about SHERLOCK is its ability to start testing without a lot of complicated and time-consuming upstream experimental work,” says Pardis Sabeti, also a co-author in the paper. In the wake of the ongoing Zika outbreak, Sabeti and the members of her lab have been working to collect samples, rapidly sequence genomes, and share data in order to accelerate the outbreak response effort. “This ability to take raw samples and immediately start processing could transform the diagnosis of Zika and a boundless number of other infectious diseases,” she says. “This is just the beginning.”

Additional authors include Jeong Wook Lee, Patrick Essletzbichler, Aaron J. Dy, Julia Joung, Vanessa Verdine, Nina Donghia, Nichole M. Daringer, Catherine A. Freije, Cameron Myhrvold, Roby P. Bhattacharyya, Jonathan Livny, and Eugene V. Koonin.

2017 Sharp Lecture: Larry Abbott

Anne Trafton |

March 20, 2017
Phillip A. Sharp Lecture in Neural Circuits
Sponsored by Biogen Idec

“Unmarring the Perceptron: Lessons in Cerebellar Computing from Fish and Flies”
Larry Abbott, Columbia University

Precise technique tracks dopamine in the brain

Anne Trafton |

MIT researchers have devised a way to measure dopamine in the brain much more precisely than previously possible, which should allow scientists to gain insight into dopamine’s roles in learning, memory, and emotion.

Dopamine is one of the many neurotransmitters that neurons in the brain use to communicate with each other. Previous systems for measuring these neurotransmitters have been limited in how long they provide accurate readings and how much of the brain they can cover. The new MIT device, an array of tiny carbon electrodes, overcomes both of those obstacles.

“Nobody has really measured neurotransmitter behavior at this spatial scale and timescale. Having a tool like this will allow us to explore potentially any neurotransmitter-related disease,” says Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the study.

Furthermore, because the array is so tiny, it has the potential to eventually be adapted for use in humans, to monitor whether therapies aimed at boosting dopamine levels are succeeding. Many human brain disorders, most notably Parkinson’s disease, are linked to dysregulation of dopamine.

“Right now deep brain stimulation is being used to treat Parkinson’s disease, and we assume that that stimulation is somehow resupplying the brain with dopamine, but no one’s really measured that,” says Helen Schwerdt, a Koch Institute postdoc and the lead author of the paper, which appears in the journal Lab on a Chip.

Studying the striatum

For this project, Cima’s lab teamed up with David H. Koch Institute Professor Robert Langer, who has a long history of drug delivery research, and Institute Professor Ann Graybiel, who has been studying dopamine’s role in the brain for decades with a particular focus on a brain region called the striatum. Dopamine-producing cells within the striatum are critical for habit formation and reward-reinforced learning.

Until now, neuroscientists have used carbon electrodes with a shaft diameter of about 100 microns to measure dopamine in the brain. However, these can only be used reliably for about a day because they produce scar tissue that interferes with the electrodes’ ability to interact with dopamine, and other types of interfering films can also form on the electrode surface over time. Furthermore, there is only about a 50 percent chance that a single electrode will end up in a spot where there is any measurable dopamine, Schwerdt says.

The MIT team designed electrodes that are only 10 microns in diameter and combined them into arrays of eight electrodes. These delicate electrodes are then wrapped in a rigid polymer called PEG, which protects them and keeps them from deflecting as they enter the brain tissue. However, the PEG is dissolved during the insertion so it does not enter the brain.

These tiny electrodes measure dopamine in the same way that the larger versions do. The researchers apply an oscillating voltage through the electrodes, and when the voltage is at a certain point, any dopamine in the vicinity undergoes an electrochemical reaction that produces a measurable electric current. Using this technique, dopamine’s presence can be monitored at millisecond timescales.

Using these arrays, the researchers demonstrated that they could monitor dopamine levels in many parts of the striatum at once.

“What motivated us to pursue this high-density array was the fact that now we have a better chance to measure dopamine in the striatum, because now we have eight or 16 probes in the striatum, rather than just one,” Schwerdt says.

The researchers found that dopamine levels vary greatly across the striatum. This was not surprising, because they did not expect the entire region to be continuously bathed in dopamine, but this variation has been difficult to demonstrate because previous methods measured only one area at a time.

How learning happens

The researchers are now conducting tests to see how long these electrodes can continue giving a measurable signal, and so far the device has kept working for up to two months. With this kind of long-term sensing, scientists should be able to track dopamine changes over long periods of time, as habits are formed or new skills are learned.

“We and other people have struggled with getting good long-term readings,” says Graybiel, who is a member of MIT’s McGovern Institute for Brain Research. “We need to be able to find out what happens to dopamine in mouse models of brain disorders, for example, or what happens to dopamine when animals learn something.”

She also hopes to learn more about the roles of structures in the striatum known as striosomes. These clusters of cells, discovered by Graybiel many years ago, are distributed throughout the striatum. Recent work from her lab suggests that striosomes are involved in making decisions that induce anxiety.

This study is part of a larger collaboration between Cima’s and Graybiel’s labs that also includes efforts to develop injectable drug-delivery devices to treat brain disorders.

“What links all these studies together is we’re trying to find a way to chemically interface with the brain,” Schwerdt says. “If we can communicate chemically with the brain, it makes our treatment or our measurement a lot more focused and selective, and we can better understand what’s going on.”

Other authors of the paper are McGovern Institute research scientists Minjung Kim, Satoko Amemori, and Hideki Shimazu; McGovern Institute postdoc Daigo Homma; McGovern Institute technical associate Tomoko Yoshida; and undergraduates Harshita Yerramreddy and Ekin Karasan.

The research was funded by the National Institutes of Health, the National Institute of Biomedical Imaging and Bioengineering, and the National Institute of Neurological Disorders and Stroke.

McGovern Institute awards 2017 Scolnick Prize to Catherine Dulac

by Charles Jennings |

The McGovern Institute for Brain Research at MIT announced today that Catherine Dulac of Harvard University is the winner of the 2017 Edward M. Scolnick Prize in Neuroscience. She was awarded the prize for her contributions to the understanding of how pheromones control brain function and behavior and the characterization of neuronal circuits underlying sex-specific behaviors. The Scolnick Prize is awarded annually by the McGovern Institute to recognize outstanding advances in any field of neuroscience.

Dulac is the Higgins Professor in the Department of Molecular and Cellular Biology at Harvard University, where she served as Department Chair from 2007-2013. She is also an investigator of the Howard Hughes Medical Institute. She received her PhD from Pierre and Marie Curie University in Paris, where she studied mechanisms of neural crest development with Nicole le Douarin at the College de France. She moved to the US in 1992 as a postdoctoral fellow in the laboratory of Richard Axel at Columbia University, and joined the Harvard faculty in 1996.

Catherine DulacDulac is best known for her discovery of pheromone receptors and downstream brain circuits controlling sex-specific behaviors. Pheromones are volatile chemical signals that play a major role in controlling mammalian behaviors, in particular social and sexual behaviors such as aggression and reproduction. Unlike odorants, which give rise to the perception of smell, and which can be learned and flexibly associated with different stimuli, the responses to pheromones are fixed and stereotypic. Pheromone responses were known to require the vomeronasal organ (VNO), a specialized part of the olfactory epithelium within the nose, but until Dulac’s work, the molecular identity of the receptors and the neuronal circuits that underlie pheromone-evoked responses had been elusive.

In work that began while she was a postdoc, Dulac set out to identify these receptors, developing novel methods for analyzing RNA from individual sensory neurons. This pioneering work not only led her to the discovery of a large family of pheromone receptor genes, but also demonstrated the feasibility of analyzing the transcriptomes of individual neurons, an approach that is now widely used to study the brain’s extraordinary complexity.

Soon after starting her own lab at Harvard, Dulac discovered a second family of pheromone receptors. Both families are distinct from odorant receptors and are expressed in characteristic spatial patterns within the VNO. Dulac went on to study the mechanism of pheromone action, identifying the ion channel TRPC2 as an essential player in the response of VNO neurons to pheromone signaling. By genetically manipulating this signaling pathway in mice, Dulac was able to show that inputs from the VNO are necessary for gender identification and for the sex-specificity of social behaviors, including mating, aggression and parenting. She was also able to trace the connections from the VNO to the brain systems that control these behaviors, and to characterize specific neuronal populations that are necessary and sufficient for specific social behaviors. In one study, for example, she identified a population of neurons within the hypothalamus that induce parenting behaviors while suppressing aggression toward the offspring that would otherwise be triggered in males by signals from the VNO.

In another recent line of work, Dulac has studied genomic imprinting, an epigenetic phenomenon by which certain genes are differentially expressed depending on whether they were inherited from the mother or the father. Dulac’s work has revealed that imprinting of brain genes is much more common than previously realized, with important implications for basic biology and for the epidemiology of brain disorders.
Among her many honors and awards, Dulac is a fellow of the American Academy of Arts and Sciences, a Chevalier de la Legion d’Honneur, a member of the French Academy of Sciences, and a member of the US National Academy of Sciences.

The McGovern Institute will award the Scolnick Prize to Dr. Dulac on Monday March 13. At 4:00pm she will deliver a lecture entitled “The Neurobiology of Social Behavior Circuits,” to be followed by a reception, at the McGovern Institute in the Brain and Cognitive Sciences Complex, 43 Vassar Street (building 46, room 3002) in Cambridge. The event is free and open to the public.

New center for autism research established at the McGovern Institute

by McGovern Institute |

The McGovern Institute is pleased to announce the establishment of a new center dedicated to autism research. The center is made possible by a kick-off commitment of $20 million, made by Lisa Yang and MIT alumnus Hock Tan ’75.

The Hock E. Tan and K. Lisa Yang Center for Autism Research will support research on the genetic, biological and neural bases of autism spectrum disorders, a developmental disability estimated to affect 1 in 68 individuals in the United States. Tan and Yang hope their initial investment will stimulate additional support and help foster collaborative research efforts to erase the devastating effects of this disorder on individuals, their families and the broader autism community.

“With the Tan-Yang Center for Autism Research, we can imagine a world in which medical science understands and supports those with autism — and we can focus MIT’s distinctive strengths on making that dream a reality. Lisa and Hock’s gift reminds us of the impact we envision for the MIT Campaign for a Better World.  I am grateful for their leadership and generosity, and inspired by the possibilities ahead,” says MIT President L. Rafael Reif.

“I am thrilled to be investing in an institution that values a multidisciplinary collaborative approach to solving complex problems such as autism,” says Hock Tan, who graduated from MIT in 1975 with a bachelor’s degree and master’s degree in mechanical engineering. “We expect that successful research originating from our Center will have a significant impact on the autism community.”

Originally from Penang, Malaysia, Tan has held several high-level finance and executive positions since leaving MIT. Tan is currently CEO of chipmaker Broadcom, Ltd.

Research at the Tan-Yang Center will focus on four major lines of investigation: genetics, neural circuits, novel autism models and the translation of basic research to the clinical setting.  By focusing research efforts on the origins of autism in our genes, in the womb and in the first years of life, the Tan-Yang Center aims to develop methods to better detect and potentially prevent autism spectrum disorders entirely. To help meet this challenge, the Center will support collaborations across multiple disciplines—from genes to neural circuits—both within and beyond MIT.

“MIT has some of the world’s leading scientists studying autism,” says McGovern Institute director Robert Desimone. “Support from the Tan-Yang Center will enable us to pursue exciting new directions that could not be funded by traditional sources. We will exploit revolutionary new tools, such as CRISPR and optogenetics, that are transforming research in neuroscience. We hope to not only identify new targets for medicines, but also develop novel treatments that are not based on standard pharmacological approaches. By supporting cutting-edge autism research here at MIT as well as our collaborative institutions, the Center holds great promise to accelerate our basic understanding of this complex disorder.”

“Millions of families have been impacted by autism,” says Yang, a longtime advocate for the rights of individuals with disabilities and learning differences. “I am profoundly hopeful that the discoveries made at the Tan-Yang Center will have a long-term impact on the field of autism research and will provide fresh answers and potential new treatments for individuals affected by this disorder.”

Sensor traces dopamine released by single cells

Anne Trafton |

MIT chemical engineers have developed an extremely sensitive detector that can track single cells’ secretion of dopamine, a brain chemical responsible for carrying messages involved in reward-motivated behavior, learning, and memory.

Using arrays of up to 20,000 tiny sensors, the researchers can monitor dopamine secretion of single neurons, allowing them to explore critical questions about dopamine dynamics. Until now, that has been very difficult to do.

“Now, in real-time, and with good spatial resolution, we can see exactly where dopamine is being released,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering and the senior author of a paper describing the research, which appears in the Proceedings of the National Academy of Sciences the week of Feb. 6.

Strano and his colleagues have already demonstrated that dopamine release occurs differently than scientists expected in a type of neural progenitor cell, helping to shed light on how dopamine may exert its effects in the brain.

The paper’s lead author is Sebastian Kruss, a former MIT postdoc who is now at Göttingen University, in Germany. Other authors are Daniel Salem and Barbara Lima, both MIT graduate students; Edward Boyden, an associate professor of biological engineering and brain and cognitive sciences, as well as a member of the MIT Media Lab and the McGovern Institute for Brain Research; Lela Vukovic, an assistant professor of chemistry at the University of Texas at El Paso; and Emma Vander Ende, a graduate student at Northwestern University.

“A global effect”

Dopamine is a neurotransmitter that plays important roles in learning, memory, and feelings of reward, which reinforce positive experiences.

Neurotransmitters allow neurons to relay messages to nearby neurons through connections known as synapses. However, unlike most other neurotransmitters, dopamine can exert its effects beyond the synapse: Not all dopamine released into a synapse is taken up by the target cell, allowing some of the chemical to diffuse away and affect other nearby cells.

“It has a local effect, which controls the signaling through the neurons, but also it has a global effect,” Strano says. “If dopamine is in the region, it influences all the neurons nearby.”

Tracking this dopamine diffusion in the brain has proven difficult. Neuroscientists have tried using electrodes that are specialized to detect dopamine, but even using the smallest electrodes available, they can place only about 20 near any given cell.

“We’re at the infancy of really understanding how these packets of chemicals move and their directionality,” says Strano, who decided to take a different approach.

Strano’s lab has previously developed sensors made from arrays of carbon nanotubes — hollow, nanometer-thick cylinders made of carbon, which naturally fluoresce when exposed to laser light. By wrapping these tubes in different proteins or DNA strands, scientists can customize them to bind to different types of molecules.

The carbon nanotube sensors used in this study are coated with a DNA sequence that makes the sensors interact with dopamine. When dopamine binds to the carbon nanotubes, they fluoresce more brightly, allowing the researchers to see exactly where the dopamine was released. The researchers deposited more than 20,000 of these nanotubes on a glass slide, creating an array that detects any dopamine secreted by a cell placed on the slide.

Dopamine diffusion

In the new PNAS study, the researchers used these dopamine sensors to explore a longstanding question about dopamine release in the brain: From which part of the cell is dopamine secreted?

To help answer that question, the researchers placed individual neural progenitor cells known as PC-12 cells onto the sensor arrays. PC-12 cells, which develop into neuron-like cells under the right conditions, have a starfish-like shape with several protrusions that resemble axons, which form synapses with other cells.

After stimulating the cells to release dopamine, the researchers found that certain dopamine sensors near the cells lit up immediately, while those farther away turned on later as the dopamine diffused away. Tracking those patterns over many seconds allowed the researchers to trace how dopamine spreads away from the cells.

Strano says one might expect to see that most of the dopamine would be released from the tips of the arms extending out from the cells. However, the researchers found that in fact more dopamine came from the sides of the arms.

“We have falsified the notion that dopamine should only be released at these regions that will eventually become the synapses,” Strano says. “This observation is counterintuitive, and it’s a new piece of information you can only obtain with a nanosensor array like this one.”

The team also showed that most of the dopamine traveled away from the cell, through protrusions extending in opposite directions. “Even though dopamine is not necessarily being released only at the tip of these protrusions, the direction of release is associated with them,” Salem says.

Other questions that could be explored using these sensors include how dopamine release is affected by the direction of input to the cell, and how the presence of nearby cells influences each cell’s dopamine release.

The research was funded by the National Science Foundation, the National Institutes of Health, a University of Illinois Center for the Physics of Living Cells Postdoctoral Fellowship, the German Research Foundation, and a Liebig Fellowship.

Rethinking mental illness treatment

by Elizabeth Dougherty |

McGovern researchers are finding neural markers that could help improve treatment for psychiatric patients.

Ten years ago, Jim and Pat Poitras committed $20M to the McGovern Institute to establish the Poitras Center for Affective Disorders Research. The Poitras family had been longtime supporters of MIT, and because they had seen mental illness in their own family, they decided to support an ambitious new program at the McGovern Institute, with the goal of understanding the fundamental biological basis of depression, bipolar disorder, schizophrenia and other major psychiatric disorders.

The gift came at an opportune time, as the field was entering a new phase of discovery, with rapid advances in psychiatric genomics and brain imaging, and with the emergence of new technologies for genome editing and for the development of animal models. Over the past ten years, the Poitras Center has supported work in each of these areas, including Feng Zhang’s work on CRISPR-based genome editing, and Guoping Feng’s work on animal models for autism, schizophrenia and other psychiatric disorders.

This reflects a long-term strategy, says Robert Desimone, director of the McGovern Institute who oversees the Poitras Center. “But we must not lose sight of the overall goal, which is to benefit human patients. Insights from animal models and genomic medicine have the potential to transform the treatments of the future, but we are also interested in the nearer term, and in what we can do right now.”

One area where technology can have a near-term impact is human brain imaging, and in collaboration with clinical researchers at McLean Hospital, Massachusetts General Hospital and other institutions, the Poitras Center has supported an ambitious program to bring human neuroimaging closer to the clinic.

Discovering psychiatry’s crystal ball

A fundamental problem in psychiatry is that there are no biological markers for diagnosing mental illness or for indicating how best to treat it. Treatment decisions are based entirely on symptoms, and doctors and their patients will typically try one treatment, then if it does not work, try another, and perhaps another. The success rates for the first treatments are often less than 50%, and finding what works for an individual patient often means a long and painful process of trial and error.

“Someday, a person will be able to go to a hospital, get a brain scan, charge it to their insurance, and know that it helped the doctor select the best treatment,” says Satra Ghosh.

McGovern research scientist Susan Whitfield-Gabrieli and her colleagues are hoping to change this picture, with the help of brain imaging. Their findings suggest that brain scans can hold valuable information for psychiatrists and their patients. “We need a paradigm shift in how we use imaging. It can be used for more than research,” says Whitfield-Gabrieli, who is a member of McGovern Investigator John Gabrieli’s lab. “It would be a really big boost to be able use it to personalize psychiatric medicine.”

One of Whitfield-Gabrieli’s goals is to find markers that can predict which treatments will work for which patients. Another is to find markers that can predict the likely risk of disease in the future, allowing doctors to intervene before symptoms first develop. All of these markers need further validation before they are ready for the clinic, but they have the potential to meet a dire need to improve treatment for psychiatric disease.

A brain at rest

For Whitfield-Gabrieli, who both collaborates with and is married to Gabrieli, that paradigm shift began when she started to study the resting brain using functional magnetic resonance imaging (fMRI). Most brain imaging studies require the subject to perform a mental task in the scanner, but these are time-consuming and often hard to replicate in a clinical setting.In contrast, resting state imaging requires no task. The subject simply lies in the scanner and lets the mind wander. The patterns of activity can reveal functional connections within the brain, and are reliably consistent from study to study.

Whitfield-Gabrieli thought resting state scanning had the potential to help patients because it is simple and easy to perform.

“Even a 5-minute scan can contain useful information that could help people,” says Satrajit Ghosh, a principal research scientist in the Gabrieli lab who works closely with Whitfield-Gabrieli.

Whitfield-Gabrieli and her clinical collaborator Larry Seidman at Harvard Medical School decided to study resting state activity in patients with schizophrenia. They found a pattern of activity strikingly different from that of typical brains. The patients showed unusually strong activity in a set of interconnected brain regions known as the default mode network, which is typically activated during introspection. It is normally suppressed when a person attends to the outside world, but schizophrenia patients failed to show this suppression.

“The patient isn’t able to toggle between internal processing and external processing the way a typical individual can,” says Whitfield-Gabrieli, whose work is supported by the Poitras Center for Affective Disorders Research.

Since then, the team has observed similar disturbances in the default network in other disorders, including depression, anxiety, bipolar disorder, and ADHD. “We knew we were onto something interesting,” says Whitfield-Gabrieli. “But we kept coming back to the question: how can brain imaging help patients?”

fMRI on patients

Many imaging studies aim to understand the biological basis of disease and ultimately to guide the development of new drugs or other treatments. But this is a long-term goal, and Whitfield-Gabrieli wanted to find ways that brain imaging could have a more immediate impact. So she and Ghosh decided to use fMRI to look at differences among individual patients, and to focus on differences in how they responded to treatment.

“It gave us something objective to measure,” explains Ghosh. “Someone goes through a treatment, and they either get better or they don’t.” The project also had appeal for Ghosh because it was an opportunity for him to use his expertise in machine learning and other computational tools to build systems-level models of the brain.

For the first study, the team decided to focus on social anxiety disorder (SAD), which is typically treated with either prescription drugs or cognitive behavioral therapy (CBT). Both are moderately effective, but many patients do not respond to the first treatment they try.

The team began with a small study to test whether scans performed before the onset of treatment could predict who would respond best to the treatment. Working with Stefan Hofmann, a clinical psychologist at Boston University, they scanned 38 SAD patients before they began a 12-week course of CBT. At the end of their treatment, the patients were evaluated for clinical improvement, and the researchers examined the scans for patterns of activity that correlated with the improvement. The results were very encouraging; it turned out that predictions based on scan data were 5-fold better than the existing methods based on severity of symptoms at the time of diagnosis.

The researchers then turned to another condition, ADHD, which presents a similar clinical challenge, in that commonly used drugs—such as Adderall or Ritalin—work well, but not for everyone. So the McGovern team began a collaboration with psychiatrist Joseph Biederman, Chief of Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD
at Massachusetts General Hospital, on a similar study, looking for markers of treatment response.

The study is still ongoing, and it will be some time before results emerge, but the researchers are optimistic. “If we could predict who would respond to which treatment and avoid months of trial and error, it would be totally transformative for ADHD,” says Biederman.

Another goal is to predict in advance who is likely to develop a given disease in the future. The researchers have scanned children who have close relatives with schizophrenia or depression, and who are therefore at increased risk of developing these disorders themselves. Surprisingly, the children show patterns of resting state connectivity similar to those of patients.

“I was really intrigued by this,” says Whitfield-Gabrieli. “Even though these children are not sick, they have the same profile as adults who are.”

Whitfield-Gabrieli and Seidman are now expanding their study through a collaboration with clinical researchers at the Shanghai Mental Institute in China, who plan to image and then follow 225 people who are showing early risk signs for schizophrenia. They hope to find markers that predict who will develop the disease and who will not.

“While there are no drugs available to prevent schizophrenia, it may be possible to reduce the risk or severity of the disorder through CBT, or through interventions that reduce stress and improve sleep and well-being,” says Whitfield-Gabrieli. “One likely key to success is early identification of those at highest risk. If we could diagnose early, we could do early interventions
and potentially prevent disorders.”

From association to prediction

The search for predictive markers represents a departure from traditional psychiatric imaging studies, in which a group of patients is compared with a control group of healthy subjects. Studies of this type can reveal average differences between the groups, which may provide clues to the underlying biology of the disease. But they don’t provide information about individual patients, and so they have not been incorporated into clinical practice.

The difference is critical for clinicians, says Biederman. “I treat individuals, not groups. To bring predictive scans to the clinic, we need to be sure the individual scan is informative for the person you are treating.”

To develop these predictions, Whitfield-Gabrieli and Ghosh must first use sophisticated computational methods such as ‘deep learning’ to identify patterns in their data and to build models that relate the patterns to the clinical outcomes. They must then show that these models can generalize beyond the original study population—for example, that predictions based on patients from Boston can be applied to patients from Shanghai. The eventual goal is a model that can analyze a previously unseen brain scan from any individual, and predict with high confidence whether that person will (for example) develop schizophrenia or respond successfully to a particular therapy.

Achieving this will be challenging, because it will require scanning and following large numbers of subjects from diverse demographic groups—thousands of people, not just tens or hundreds
as in most clinical studies. Collaborations with large hospitals, such as the one in Shanghai, can help. Whitfield-Gabrieli has also received funding to collect imaging, clinical, and behavioral
data from over 200 adolescents with depression and anxiety, as part of the National Institutes of Health’s Human Connectome effort. These data, collected in collaboration with clinicians at
McLean Hospital, MGH and Boston University, will be available not only for the Gabrieli team, but for researchers anywhere to analyze. This is important, because no one team or center can
do it alone, says Ghosh. “Data must be collected by many and shared by all.”

The ultimate goal is to study as many patients as possible now so that the tools can help many more later. “Someday, a person will be able to go to a hospital, get a brain scan, charge it to their insurance, and know that it helped the doctor select the best treatment,” says Ghosh. “We’re still far away from that. But that is what we want to work towards.”