Contact: Diana Kenney
dkenney@mbl.edu; 508-289-7139

WOODS HOLE, Mass. – Since artificial intelligence pioneer Marvin Minsky patented the principle of confocal microscopy in 1957, it has become the workhorse standard in life science laboratories worldwide, due to its superior contrast over traditional wide-field microscopy. Yet confocal microscopes aren’t perfect. They boost resolution by imaging just one, single, in-focus point at a time, so it can take quite a while to scan an entire, delicate biological sample, exposing it to light dosages that can be toxic.

To push confocal imaging to an unprecedented level of performance, a collaboration at the Marine Biological Laboratory (MBL) has invented a “kitchen sink” confocal platform that borrows solutions from other high-powered imaging systems, adds a unifying thread of “Deep Learning” artificial intelligence algorithms, and successfully improves the confocal’s volumetric resolution by more than 10-fold while simultaneously reducing phototoxicity. Their report on the technology, called “Multiview Confocal Super-Resolution Microscopy,” is published online today in Nature (print publication Dec. 9).

“Many labs have confocals, and if they can eke more performance out of them using these artificial intelligence algorithms, then they don’t have to invest in a whole new microscope. To me, that’s one of the best and most exciting reasons to adopt these AI methods,” said senior author and MBL Fellow Hari Shroff of the National Institute of Biomedical Imaging and Bioengineering.

Among its innovations, the new confocal platform uses three objective lenses, allowing one to image a wide variety of sample sizes, from nuclei and neurons in the C. elegans embryo to the whole adult worm. Multiple specimen views are rapidly captured, registered and fused to yield reconstructions with improved resolution over single-view confocal microscopy. The platform also introduces innovative scan heads for the three lenses, allowing line-scanning illumination to be easily added to the microscope base.

Moreover, the team added “super-resolution” capacity to the platform (enhanced resolution beyond the diffraction limit of light) by adapting techniques from structured illumination microscopy.

“The hardware summit that gets climbed in this platform is the multiple lenses around the sample, and then the super-resolution trick, which takes a combination of hardware and computation to achieve. It’s a tour de force, but it’s a pretty phototoxic recipe. There’s a lot of light being delivered to the sample,” said co-author and MBL Fellow Patrick La Rivière of the University of Chicago.

One way to address phototoxicity is to lower the light coming from the microscope’s laser. But then you begin having problems with “noise” in the image -- background graininess that can obscure fine details of the object you want to image (the “signal”). This is where artificial intelligence comes in.
 

Mouse esophageal tissue slab (XY image), immunostained for tubulin (cyan) and actin (magenta), imaged in triple-view SIM mode. On right, anatomical regions are highlighted. Credit: Yicong Wu and Xiaofei Han et al, Nature, 2021.

 
The team trained a Deep Learning computer model, or neural network, to distinguish between poorer-quality images with a low signal-to-noise ratio (SNR) and better images with a higher SNR. “Eventually the network could predict the higher SNR images, even given a fairly low SNR input,” Shroff said.

“Deep Learning allows you to take this hardware summit as the gold standard for resolution and then train a neural network to achieve similar results with much lower SNR data, many fewer acquisitions, and so much less light dose to the sample,” La Rivière said.

The team demonstrated the platform’s capabilities on more than 20 different fixed and live samples, targeting structures that ranged from less than 100 nanometers to a millimeter in size. These included protein distributions in single cells; nuclei and developing neurons in C. elegans embryos, larvae and adults; myoblasts in Drosophila wing imaginal disks, and mouse renal, esophageal, cardiac, and brain tissues. They also see potential applications for imaging human tissue in histology and pathology labs.

Shroff, La Rivière and co-author and cell biologist Daniel Colón-Ramos of Yale School of Medicine have been collaborating at MBL for nearly a decade to develop imaging technologies with higher speed, resolution and longer duration. Collaborators on this confocal platform also included Applied Scientific Instrumentation, a company they worked with both at MBL and at the National Institutes of Health.

Yicong Wu, first author on the paper, built the new confocal platform and deployed its Deep Learning approaches. Wu learned how to use Deep Learning at the MBL in the pilot version of a new course launched this year, DL@MBL: Deep Learning for Microscopy Image Analysis. (La Rivière is a faculty member in the course.)

“It’s a testament to the course that Yicong could learn Deep Learning methods in 4 days and quickly innovate with them, so we can now apply them in our lab,” Shroff said. “That’s a short feedback scheme, right? It was great that MBL catalyzed it.”

Citation:

Yicong Wu and Xiaofei Han, et al. (2021) Multiview Confocal Super-Resolution Microscopy. Nature, DOI: 10.1038/s41586-021-04110-0.

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The Marine Biological Laboratory (MBL) is dedicated to scientific discovery – exploring fundamental biology, understanding marine biodiversity and the environment, and informing the human condition through research and education. Founded in Woods Hole, Massachusetts in 1888, the MBL is a private, nonprofit institution and an affiliate of the University of Chicago.

WOODS HOLE, Mass. – ResilientWoodsHole, a public-private partnership readying for the impacts of climate change in Woods Hole, will hold a free, public symposium on Thursday, November 18, 6:30-8:30 PM, in Clapp Auditorium, Marine Biological Laboratory, 7 MBL Street, Woods Hole.

 Registration for virtual or in-person attendance is here.

ResilientWoodsHole’s mission is to secure the future of the vibrant, blue-economy village of Woods Hole in the face of major challenges and threats -- both to village infrastructure and the natural habitat -- associated with climate change.

Jump-started by three local science organizations (Woods Hole Oceanographic Institution, Marine Biological Laboratory, and NOAA Northeast Fisheries Science Center), ResilientWoodsHole has now expanded to include key local stakeholders, including the business community, the homeowner community, and the Town of Falmouth.

The three science organizations have developed assessments of potential impacts of sea-level rise and coastal storms, which were delivered in a September 2020 symposium, “Rising Tides: Phase I.”

In the upcoming symposium (Phase II), attendees will get a look inside the climate science work happening in Woods Hole; be introduced to local, regional, and statewide climate resiliency planning efforts; and become engaged around local challenges, ongoing work, and potential solutions.

The symposium will include three panel discussions:

• I. Climate Science Panel with scientists from Woods Hole Oceanographic Institution, Marine Biological Laboratory, and NOAA Northeast Fisheries Science Center
• II. Planning and Resources Panel with representatives from the state’s Office of Coastal Zone Management; the Cape Cod Commission; and the Town of Falmouth
• III. ResilientWoodsHole Initiative Panel presenting results to date and expected work to come in Phase III. A key outcome of Phase III will be a comprehensive, phased strategy for increasing coastal resiliency, including village-wide design concepts and planning.
• IV. Q & A with audience members

In-person attendance will be limited to the first 225 registrants to accommodate social distancing, and attendees must be masked. Parking will be available in any MBL lot. For more information, please contact: info@resilientwoodshole.org.

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“As an inaugural course, you’ve made history at MBL!” said Education Director Linda Hyman to the participants in the first “Deep Learning for Microscopy Image Analysis” course, held this fall.

Aligning with MBL’s other research training courses, where students tackle real, challenging problems in a scientific field, the “DL@MBL” students knew little about Deep Learning when they arrived – but could apply it to their own imaging data when they left.

“Everyone is learning so much – the faculty and teaching assistants as well as the students,” said Arlo Sheridan of the Salk Institute, a TA for the course. “It’s been amazing.”

Deep Learning, a type of machine learning, may sound exotic, but it’s quickly become a gold standard approach for making sense of the huge amount of imaging data that modern microscopes can generate.

“Imaging the neurons in one, small fruit fly brain can generate 500 terabytes of data. Just looking at all those images would take 60 years of your life without sleeping, without going on vacation,” said Jan Funke of HHMI Janelia Research Campus, co-director of DL@MBL. “That would be very painful!”

The 2021 Deep Learning course participants (names listed at bottom of page).

Find Me All Images of Cats

Inspired by the structure of the human brain, Deep Learning uses computer models called “deep neural networks” to analyze data, in this case enormous volumes of biological images.

The scientist first has to train the model to produce a desired output. If they want it to detect images of cats, for instance, they will feed the model hundreds, thousands or millions of images in which cats have been “painted,” or annotated, as well as images with no cats. After many iterations of training, the model will be able to correctly predict cats in data it’s never seen before.

“It’s quite surprising, actually, how well it works. And it’s fair to say we don’t really understand how it works,” Funke said. “It has something to do with the layers inside the network. Just as in our brains, where neurons are organized in different layers [the visual cortex has six layers, for example, where depth perception, color, motion, etc., are processed], deep neural networks keep forwarding information from one layer to the next. The more layers the network has, the more correlations it makes between your input data and whatever you’ve trained it the output should be. And that makes it successful.”

Deep Learning models can be trained to classify images (i.e., cats or no cats), detect objects within images (only cats with long fur), track objects over time (cats running), and clean up blurred or noisy images, among other functions that would be extremely tedious or impossible to accomplish by visual analysis.

The 24 students, most of them biologists, were asked to bring their own imaging data to the course. After five days of lectures and exercises in Deep Learning concepts, methods and tools, the students worked closely with faculty and TAs to apply their new skills to their own data.

“We identified promising datasets and grouped students with similar questions together,” Funke said. “Not all of the datasets were ready to be used for Deep Learning, which requires a lot of annotated training examples. So, a few groups worked with publicly available datasets that were similar to their own, so they can apply the methods once their own data is ready for training.”

Members of the “Instance Segmentation” group are thrilled to see the neural network they had trained perform successfully. Left to right: Students Anh Phuong Le (Harvard Medical School), Sandy Nandagopal (Harvard Medical School), Xiaolei Wang (Duke University) and teaching assistant Arlo Sheridan (Salk Institute). Credit: Diana Kenney

Members of the team that classified cell cycle phases in their image dataset. Left to right: Suganya Sivagurunathan (Northwestern University), Shruthi Bandyakda (Boston University) and Hope Healey (University of Oregon). Credit: Diana Kenney

(Neural) Networking for Success

One afternoon in Loeb Laboratory, DL@MBL looked as intense and exciting as any MBL course. The students were clustered in groups, cramming to prepare their data to present in the course’s final symposium. Suddenly a wave of excitement rippled through the self-named “Instance Gratification” group.

“Thirty minutes before their presentation, they got the neural network to do exactly what they wanted it to do!” crowed Sheridan.

One of the students, Xiaolei Wang from Duke University, had brought movies of Drosophila epithelial cells growing and shrinking during morphogenesis, along with 100 annotated “ground truth” images of these cells and of another type of cell. Together with Sheridan and the rest of her group, they used the ground-truth images to train a deep neural network to recognize the difference between the two kinds of cells (called semantic segmentation), and assign each cell a unique label (instance segmentation). They went through 50,000 iterations of training, which took about 4 hours. What happened at the 11^th hour is just what they hoped: The model successfully segmented the epithelial cells and tracked them over time. (The key was to run the model in 2D for each step in time, and then connect the labels between time sets to derive a final 3D segmentation.)

“There’s a million different directions they can go now,” Sheridan said. “In their heads they are going, ‘Oh, now I want to segment everything!’”

Another group trained a neural network to classify cells according to which phase of the cell cycle they were in. And the “Pumpkin Spice” group trained a neural network to detect branch-like structures in imaging data, such as vasculature in the retina or long, filamentous mitochondria.

“The project phase of the course was definitely its most successful aspect, compared with our pilot course in 2019,” Funke said. Sheridan agreed. “When the students apply what they learn to their own data – stuff they care about -- after they go home they can apply it and also teach it to other people,” he said.

“I came to MBL with one problem and got 10 problems to bring back. But I can't believe I was with zero background on Deep Learning and now can actually write and run a model!” wrote student Anh Phuong Le on Twitter.

The pandemic restricted many of the faculty’s attendance to virtual, but they hope next year to be on the ground in Woods Hole.

“It was horrible to be on my couch instead of in the midst of all of you!” said course co-director Florian Jug of Human Technopole in Milan, Italy. “For a lecture, remote teaching is OK, but to guide the students beyond a 90-minute session, you really need to be on-site,” he said. Course co-director Anna Kreshuk of EMBL Heidelberg also looks forward to in-person attendance at the 2022 course, to begin in late August.

The costs of participating in the Deep Learning course were generously provided by the Howard Hughes Medical Institute, the Chan Zuckerberg Initiative, and the National Center for Brain Mapping.

2021 Deep Learning course participants (pictured above). Students: Shruthi Bandyadka, Benjamin Asher Barad, Sayantanee Biswas, Kenny Kwok Hin Chung, Steven Del Signore, Luke Funk, Samuel Gonzalez, Hope Meredith Healey, Anh Phuong Le, Matthew Loring, Nagarajan Nandagopal, Abigail Carson Neininger, Leti Nunez, Augusto Ortega Granillo, Puebla Ramirez, Tabita Shamayim, Xiongtao Ruan, Gabriela Salazar Lopez, Suganya Sivagurunathan, Benjamin Sterling, Yuqi Tan, Linh Thi Thuy Trinh, Shixing Wang, Xiaolei Wang, Zachary Whiddon. Teaching Assistants: Mehdi Azabou, Caroline Malin-Mayor, William Patton, Morgan Schwartz, Arlo Sheridan, Steffen Wolf. Faculty: Jan Funke, David van Valen. Not shown in class photo but present: at MBL teaching assistants Larissa Heinrich and Tri Nguyen; faculty Shalin Mehta.

The Instance Segmentation group presents its data. Credit: Diana Kenney

By Diana Kenney

Fresh ideas propel scientific discovery, but so do inventions, as the career of Elizabeth ‘Liz’ Jonas vividly shows. And Jonas’s invention happened in classic Marine Biological Laboratory (MBL) style: with salvaged sculpture wire, purple tape, and dashes of ingenuity, collaboration and laughter. It enabled years of future research that eventually led her to close in on an elusive accomplice in cell death associated with many serious disorders, including neurodegenerative disease.

Jonas was an undergraduate at Yale in 1980 when she got the chance to spend a summer at the MBL observing neuroscientist Rodolfo Llinas of New York University. Llinas was describing the properties of neurotransmission -- the electro-chemical conversation that passes between neurons  -- using Woods Hole squid as his research organism. In squid, there is one communication junction between neurons that is “giant”– it’s 200 times larger than the largest human synapse -- making this an ideal organism for studying neurotransmission. (See animation.)

“It was a really exciting summer,” Jonas says. “The squid boat (then the Loligo; today it’s the Gemma) would roll in between 11:30 AM and 3 PM with the day’s catch, and then the scientists would stay up all night doing experiments. I learned a tremendous amount and I just loved every minute of it.”

Len Kaczmarek and Liz Jonas of Yale University in their Whitman Center lab at MBL in 2001. Credit: J. Marie Hardwick

Jonas was hooked. After earning her M.D. and completing her residency training in Neurology, Jonas joined the lab of Leonard (Len) Kaczmarek at Yale as a postdoctoral scientist. Here, she worked with Kaczmarek on cutting-edge investigations of the class of proteins called ion channels that regulate a neuron’s excitability, or its ability to conduct charge. Kaczmarek co-directed the MBL Neurobiology course for five summers in the early ‘90s.

A Crazy Idea

The next summer, Jonas had what she calls a “crazy idea” and convinced Kaczmarek to return to the MBL Whitman Center to explore it. “It wasn’t difficult,” says Kaczmarek. “My family wanted to come back. My kids had grown up here. They love Woods Hole.”

Jonas’s idea? Rather than recording electrical current across the neuron’s outer membrane, Jonas wanted to try and record conductances inside the neuron -- from the membranes of its tiny, internal organelles.

“We started working on this at Yale, but we realized that a nice preparation in which to try this would be squid,” Jonas says. And the only place to get squid was the MBL.

Once back in Woods Hole in an MBL lab, they zoomed in on recording from organelles inside the squid presynaptic terminal, where neurotransmitter is packaged for release.

Jonas had a problem, however. Her idea called for two electrodes that move relative to each other, but she couldn’t find a way to apply the force needed for them to move.

“We finally solved that problem using a piece of sculpture wire that Len’s wife had as part of her art supplies,” Jonas said, “and some purple tape. Steven Zottoli, a neuroscientist at MBL from Williams College, subsequently claimed that it was his roll of purple tape! He put up notices all over the lab stating he had invented the Purple Tape Wire. That became a playful bone of contention -- Now we’d have to get the legal teams involved! We had a lot of fun with that.”
 

Liz Jonas dissecting out the squid giant synapse at the MBL in the early 2000s. Credit: J. Marie Hardwick

An electrophysiology rig for recording electrical activity from neurons in the Jonas-Kaczmarek-Hardwick lab in 2001. Photo: J. Marie Hardwick

 
The Life and Death of a Neuron

Jonas’s technique opened a whole new world of inquiry inside neurons. She began recording from “very unusual, large-conductance ion channels” within the squid presynaptic terminal. With Joanne Buchanan at MBL, she eventually found that the recordings were coming from mitochondrial membranes. In nerve cells, mitochondria supply the energy for neurotransmission, among other functions. Most intriguingly, it was known that if a particular channel on the mitochondrial membrane remains open too long -- in any type of cell -- the cell will die.

“Various kinds of stresses, such as the lack of blood supply known as ischemia, can open this channel,” says Jonas. “It’s involved in diseases of the nervous system, heart, kidney, liver, muscle, pancreas; in diabetes, cancer – almost any organ you can think of,” she says.

But at the time nobody knew – and it’s still debated today – what membrane proteins form this important channel, known as the mitochondrial permeability transition pore (mPTP).

“It’s one of the Holy Grails of mitochondrial biology. What is the molecular identity of the mPTP?” says J. Marie Hardwick of Johns Hopkins Medicine, whose lab studies cell suicide and its role in disease.

Cell suicide (programmed cell death) is part of the normal development of an organism, as unneeded cells are pruned out. But in neurodegenerative disease, “cells die when they aren’t supposed to. The other side of that is cancer, when tumor cells manage to avoid cell death,” Hardwick says.

Jonas joined the Yale faculty in 2002 and dedicated a portion of her lab work to tracking down the ion channel associated with the mPTP. Soon after, Hardwick began coming regularly to MBL to collaborate in the quest, at the suggestion of her and Kaczmarek’s colleague, John Hickman. Hardwick is particularly interested in a family of proteins, called Bcl-2, known to regulate cell death.

“It took three summers before we could publish our first joint paper on this, because the squid weren’t available anywhere else,” says Hardwick. “We had a big tub of squid in the lab and many messy experiments.” In true MBL fashion, they collaborated with each other and MBL technical staff to supply materials and equipment, perform experiments and interpret the data. “Those were very fun times,” Hardwick remembers.

The Woods Hole squid (Doryteuthis pealeii) has a giant synapse – 200 times bigger than the largest human synapse -- making it an ideal organism for studying neurotransmission. Credit: Roger Hanlon

The team identified a candidate for the mPTP in 2011 and 2014, though their findings were controversial.

“Liz came under fire for this, but my perception now is that a lot of people are paying attention to it. Liz gets it right. She is a very daring scientist,” Hardwick says.

“We know for a fact that we’ve identified the largest-conductance channel on the mitochondrial inner membrane to date. But at this point, to say this is the only mPTP would be incorrect,” says Jonas. “There are many channels, and they are all participating in permeability of the membrane. The question is: Which are involved in the complex phenomenon of mitochondrial permeability transition, and how do those channels work together?”

Jonas’s lab is actively collaborating to develop drugs to close the mPTP under pathological conditions, as are other labs around the world.

Equally important, Jonas’s lab has unearthed surprising fundamentals about the role of the mPTP.

“Not only is it a cause of cell death, we’ve discovered it’s a major regulator of metabolism,” she says. “It regulates body weight, temperature, and the metabolic switches that can activate cells.” Jonas published her first big paper on regulation of neuronal developmental metabolism by the putative mPTP in Cell, 2020.

MBL as a Second Home

While Jonas’s lab stopped using squid several years go (it now uses mammalian systems), she and Kaczmarek still spend every summer at MBL “because we love it here,” Jonas says. They and their families have raised children here, and they’ve trained countless students in their MBL lab. Many of their students learn electrophysiology (a technique few labs now perform), but also cell imaging. These students are learning “from the best,” Hardwick says.
 

The Jonas-Kaczmarek-Hardwick lab at MBL is often bursting with student trainees, as shown in this photo from 2006. “We’ve had as many as 10 or 11 students in our lab” over the course of a summer, says Jonas. Credit: J. Marie Hardwick

Liz Jonas, Steve Zottoli (center, trustee of the Grass Foundation/Grass Lab at MBL), and Len Kaczmarek with students in 2006. Credit: J. Marie Hardwick

 
“What’s exciting about electrophysiology is you see the brain working,” says Kaczmarek. “You see channels opening, neurons firing, ensembles of neurons. And you see your results during the experiment.”

Their labs enjoy taking multiple approaches: electrophysiology, imaging, biochemistry, genetics and computation. “We believe that if you want to solve a problem, you’ve got to find the technique that will help you solve it,” Jonas says.

Hardwick also comes back to MBL each summer, family in tow, to continue her fruitful collaborations. In recent years, she has taken a keen interest in whether single-celled organisms, such as yeast and microbes, also undergo cell suicide – a provocative idea that has caught the interest of other scientists at MBL.

“The MBL enables you to really appreciate collaborations, teaching and mentoring,” Jonas says.

Len Kaczmarek and Liz Jonas of Yale University and J. Marie Hardwick of Johns Hopkins Medicine in their Whitman Center lab at MBL in 2021. Photo courtesy of J. Marie Hardwick

References:

Jonas EA, Buchanan J, Kaczmarek LK (1999) Prolonged activation of mitochondrial conductances during synaptic transmission. Science. doi: 10.1126/science.286.5443.1347.

Jonas EA, Hickman JA, Hardwick JM, and Kaczmarek LK (2004) Exposure to hypoxia rapidly induces mitochondrial channel activity within a living synapse. J. Biol. Chem. doi: 10.1074/jbc.M410661200

Jonas EA et al (2004) Proapoptotic N-truncated BCL-xL protein activates endogenous mitochondrial channels in living synaptic terminals. Proc. Natl. Acad. Sci.  doi: 10.1073/pnas.0401372101

Chen YB et al (2011) Bcl-xL regulates mitochondrial energetics by stabilizing the inner membrane potential. J. Cell Biol. doi: 10.1083/jcb.201108059

Hardwick JM, Chen Y and Jonas AE (2012) Multipolar functions of BCL-2 proteins link energetics to apoptosis. Trends Cell Biol. doi: 10.1016/j.tcb.2012.03.005

Alavian KN, et al (2014) An uncoupling channel within the c-subunit ring of the F1FO ATP synthase is the mitochondrial permeability transition pore. Proc Natl Acad Sci U S A doi: 10.1073/pnas.1401591111

Kaczmarek LK, Zhang Y (2017) Kv3 channels: Enablers of rapid firing, neurotransmitter release, and neuronal endurance. Physiol Rev. doi: 10.1152/physrev.00002.2017.

Kulkarni M, Stolp ZD and Hardwick JM (2019) Targeting intrinsic cell death pathways to control fungal pathogens. Biochem. Pharmacol. doi: 10.1016/j.bcp.2019.01.012

Licznerski P et al (2020) ATP Synthase c-subunit leak causes aberrant cellular metabolism in Fragile X Syndrome. Cell, doi: 10.1016/j.cell.2020.07.008

WOODS HOLE, Mass. — For two people to communicate in a loud, crowded room, they need to be standing side by side. The same is often true for neurons in the brain. But the same way a cell phone allows two people to communicate clearly across the room, new research at the Marine Biological Laboratory (MBL) opened up a new channel of communication in the worm C. elegans brain.

The research, published in Nature Communications, highlights the development of HySyn, a system designed to synthetically reconnect neural circuits using neuropeptides from Hydra, a small, freshwater organism. (Neuropeptides modulate the activity of neurotransmitters to increase or decrease the strength of impulses between neurons.)

For the first time, the researchers created genetic lines of mutant C. elegans that expressed neuropeptides from the Hydra brain, creating an artificial synapse that rewired a behavioral circuit in the worm. Because none of the other synapses in the brain, besides those fitted with the hydra receptor and neuropeptide, could hear the “command,” it was like giving them a cell phone so they could communicate.

C. elegans worms with green fluorescent protein (GFP) inserted into their neurons to visualize neural development. Photo Credit: Heiti Paves, Wikimedia Commons.

“These neuromodulatory peptides let you communicate at a distance,” said MBL Fellow Daniel Colón-Ramos of Yale University School of Medicine. “It gives you more flexibility as a researcher to manipulate neurons that are not adjacent to each other.”  Colón-Ramos, senior author on the paper, was postdoctoral advisor for the paper’s first author, former MBL Grass Fellow Josh Hawk. The work and analysis was performed at the MBL and at Yale University in Colón-Ramos’s lab.

The researchers used a mutant line of C. elegans that was missing the neural connection that controlled specific behavior — the behavior that told them that they were full and needed to stop searching for food. By taking genes that encode a neuropeptide and its receptor from Hydra and putting them into the C. elegans worm, researchers were able to restore the neural circuit that controls this behavior. They created two separate genetic lines—one that contained the neuropeptide and one that contained the receptor. The offspring of the pair contained the full neural peptide pathway. But, according to Hawk, it’s just one possible pathway to focus on.

“There are hundreds of neural peptides in Hydra, each of which could be a different channel of communication,” said Hawk. “To me, that’s the most exciting thing. This should open up a whole area that no one has ever explored before.”

Hawk called this study a “proof of principle” for the HySyn tool. Unlike most organisms, Hydra don’t have a classic neurotransmitter system in the brain. Instead, they rely completely on a net of neuropeptides. Each of these Hydra neuropeptides has the opportunity for a unique line of communication. Hawk focused on a particular neuropeptide that creates a slow-building signal, like the slow-building sensation of fullness as you eat a meal. Connecting different neurons with this neuropeptide could create a slowly rising pain response or strengthen a new memory.

Josh Hawk at the MBL as a Grass Foundation Fellow in 2019. Photo Credit: Melissa Coleman

There are many different types of communication in the brain. Some are fast, on the order of milliseconds. But others are slower. That’s what Hawk was focusing on, the slow neuromodulatory response that told the C. elegans: “I’m full, don’t go seek out new food.” The researchers created an artificial, neuromodulatory synapse that told the C. elegans it was time to relax.

“Usually, we can break things in science. That’s very powerful. When we break things we can see what they are necessary for, which also tells us a little about how they work. But to really understand how they work, you want to know if you can rebuild them—fix them—after they are broken. And that is very hard to do,” said Colón-Ramos. “That [Hawk] could rebuild the connections between cells was very exciting and innovative.”

Hawk wasn’t just putting back missing components in the C. elegans brain; he was putting in entirely new components from Hydra -- fixing what was broken with a different set of parts. It showed the researchers that it wasn’t necessarily the identity of the parts themselves, but the communication and modulation between the synapses that regulated the behavior.

Hawk said he hopes future research focuses on knocking out lines of communication in the brain and rebuilding them in a different way with HySyn.  “This is the beginning of a set of tools, and as those tools are expanded, it gives us a real ability to tweak connections in the brain in a variety of permutations,” he said.

Hydra are freshwater organisms, only a few millimeters in length. Here, muscle fibers are in sepia and the nerve net is in blue/green.
Credit: John Szymanski, Current Biology

“There’s a lot of diversity of synaptic connection in any animal’s brain. Being able to pick and choose what to put in another organism will help us untangle and understand how and why brains do what they do,” said Hawk.

The researchers are confident that HySyn will work in a variety of organisms. They tested it in vertebrate cells as well as in C. elegans.

Hawk added that he hopes researchers will focus on learning about the strength of connections within the brain. “In theory, can you tear down the worm brain and then rebuild it and find out what is lost when you rebuild it certain ways. It’s what we did as kids: If you want to know how something works, you break it down and rebuild it. But we need more tools to do that.” HySin  represents a contribution to that toolset.

In addition to his time as a Grass Fellow in 2019, Hawk was faculty member for the MBL’s Neural Systems and Behavior (NS&B) course from 2015-2019.

“Josh was able to leverage knowledge from the MBL and the people he met and put all that knowledge together, with his own interests in neuroscience, and make a significant contribution.  This is what programs like the Grass Fellowship and MBL help catalyze in science,” said Colón-Ramos.

“It’s like an emblematic MBL project,” Colon-Ramos said. “High risk, high reward. Lots of fun and bringing in a lot of knowledge from the MBL community.”

Citation:

Hawk, J.D., Wisdom, E.M., Sengupta, T. et al. (2021) A genetically encoded tool for reconstituting synthetic modulatory neurotransmission and reconnect neural circuits in vivo. Nature Communications, DOI: 10.1038/s41467-021-24690-9

Homepage photo:

The C. elegans worm is a common model organism. Photo Credit: Zeynep F. Altun, Wikimedia Commons