Scientists want the salamander's secret: how they regenerate tissue
All organisms have the capacity to repair or regenerate tissue damage. None can do it better than salamanders or newts, which can regenerate an entire severed limb.
That feat has amazed and delighted man from the dawn of time and led to endless attempts to understand how it happens – and whether we can control it for our own purposes. An exciting new clue toward that understanding has come from a surprising source: research on the decline of cells, called cellular senescence.
Senescence is the last stage in the life of a cell. Whereas some cells simply break up or wither and die off, others transition into a zombie-like state where they can no longer divide. In this liminal phase, the cell still pumps out many different molecules that can affect its neighbors and cause low grade inflammation. Senescence is associated with many of the declining biological functions that characterize aging, such as inflammation and genomic instability.
Oddly enough, newts are one of the few species that do not accumulate senescent cells as they age, according to research over several years by Maximina Yun. A research group leader at the Center for Regenerative Therapies Dresden and the Max Planck Institute of Molecular and Cell Biology and Genetics, in Dresden, Germany, Yun discovered that senescent cells were induced at some stages of regeneration of the salamander limb, “and then, as the regeneration progresses, they disappeared, they were eliminated by the immune system,” she says. “They were present at particular times and then they disappeared.”
Senescent cells added to the edges of the wound helped the healthy muscle cells to “dedifferentiate,” essentially turning back the developmental clock of those cells into more primitive states.
Previous research on senescence in aging had suggested, logically enough, that applying those cells to the stump of a newly severed salamander limb would slow or even stop its regeneration. But Yun stood that idea on its head. She theorized that senescent cells might also play a role in newt limb regeneration, and she tested it by both adding and removing senescent cells from her animals. It turned out she was right, as the newt limbs grew back faster than normal when more senescent cells were included.
Senescent cells added to the edges of the wound helped the healthy muscle cells to “dedifferentiate,” essentially turning back the developmental clock of those cells into more primitive states, which could then be turned into progenitors, a cell type in between stem cells and specialized cells, needed to regrow the muscle tissue of the missing limb. “We think that this ability to dedifferentiate is intrinsically a big part of why salamanders can regenerate all these very complex structures, which other organisms cannot,” she explains.
Yun sees regeneration as a two part problem. First, the cells must be able to sense that their neighbors from the lost limb are not there anymore. Second, they need to be able to produce the intermediary progenitors for regeneration, , to form what is missing. “Molecularly, that must be encoded like a 3D map,” she says, otherwise the new tissue might grow back as a blob, or liver, or fin instead of a limb.
Wound healing
Another recent study, this time at the Mayo Clinic, provides evidence supporting the role of senescent cells in regeneration. Looking closely at molecules that send information between cells in the wound of a mouse, the researchers found that senescent cells appeared near the start of the healing process and then disappeared as healing progressed. In contrast, persistent senescent cells were the hallmark of a chronic wound that did not heal properly. The function and significance of senescence cells depended on both the timing and the context of their environment.
The paper suggests that senescent cells are not all the same. That has become clearer as researchers have been able to identify protein markers on the surface of some senescent cells. The patterns of these proteins differ for some senescent cells compared to others. In biology, such physical differences suggest functional differences, so it is becoming increasingly likely there are subsets of senescent cells with differing functions that have not yet been identified.
There are disagreements within the research community as to whether newts have acquired their regenerative capacity through a unique evolutionary change, or if other animals, including humans, retain this capacity buried somewhere in their genes.
Scientists initially thought that senescent cells couldn’t play a role in regeneration because they could no longer reproduce, says Anthony Atala, a practicing surgeon and bioengineer who leads the Wake Forest Institute for Regenerative Medicine in North Carolina. But Yun’s study points in the other direction. “What this paper shows clearly is that these cells have the potential to be involved in tissue regeneration [in newts]. The question becomes, will these cells be able to do the same in humans.”
As our knowledge of senescent cells increases, Atala thinks we need to embrace a new analogy to help understand them: humans in retirement. They “have acquired a lot of wisdom throughout their whole life and they can help younger people and mentor them to grow to their full potential. We're seeing the same thing with these cells,” he says. They are no longer putting energy into their own reproduction, but the signaling molecules they secrete “can help other cells around them to regenerate.”
There are disagreements within the research community as to whether newts have acquired their regenerative capacity through a unique evolutionary change, or if other animals, including humans, retain this capacity buried somewhere in their genes. If so, it seems that our genes are unable to express this ability, perhaps as part of a tradeoff in acquiring other traits. It is a fertile area of research.
Dedifferentiation is likely to become an important process in the field of regenerative medicine. One extreme example: a lab has been able to turn back the clock and reprogram adult male skin cells into female eggs, a potential milestone in reproductive health. It will be more difficult to control just how far back one wishes to go in the cell's dedifferentiation – part way or all the way back into a stem cell – and then direct it down a different developmental pathway. Yun is optimistic we can learn these tricks from newts.
Senolytics
A growing field of research is using drugs called senolytics to remove senescent cells and slow or even reverse disease of aging.
“Senolytics are great, but senolytics target different types of senescence,” Yun says. “If senescent cells have positive effects in the context of regeneration, of wound healing, then maybe at the beginning of the regeneration process, you may not want to take them out for a little while.”
“If you look at pretty much all biological systems, too little or too much of something can be bad, you have to be in that central zone” and at the proper time, says Atala. “That's true for proteins, sugars, and the drugs that you take. I think the same thing is true for these cells. Why would they be different?”
Our growing understanding that senescence is not a single thing but a variety of things likely means that effective senolytic drugs will not resemble a single sledge hammer but more a carefully manipulated scalpel where some types of senescent cells are removed while others are added. Combinations and timing could be crucial, meaning the difference between regenerating healthy tissue, a scar, or worse.
Autonomous, indoor farming gives a boost to crops
The glass-encased cabinet looks like a display meant to hold reasonably priced watches, or drugstore beauty creams shipped from France. But instead of this stagnant merchandise, each of its five shelves is overgrown with leaves — moss-soft pea sprouts, spikes of Lolla rosa lettuces, pale bok choy, dark kale, purple basil or red-veined sorrel or green wisps of dill. The glass structure isn’t a cabinet, but rather a “micro farm.”
The gadget is on display at the Richmond, Virginia headquarters of Babylon Micro-Farms, a company that aims to make indoor farming in the U.S. more accessible and sustainable. Babylon’s soilless hydroponic growing system, which feeds plants via nutrient-enriched water, allows chefs on cruise ships, cafeterias and elsewhere to provide home-grown produce to patrons, just seconds after it’s harvested. Currently, there are over 200 functioning systems, either sold or leased to customers, and more of them are on the way.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Scientists make progress with growing organs for transplants
Story by Big Think
For over a century, scientists have dreamed of growing human organs sans humans. This technology could put an end to the scarcity of organs for transplants. But that’s just the tip of the iceberg. The capability to grow fully functional organs would revolutionize research. For example, scientists could observe mysterious biological processes, such as how human cells and organs develop a disease and respond (or fail to respond) to medication without involving human subjects.
Recently, a team of researchers from the University of Cambridge has laid the foundations not just for growing functional organs but functional synthetic embryos capable of developing a beating heart, gut, and brain. Their report was published in Nature.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.