Next year, a neurologist will test CRISPR base editing in a trial of five people with muscular dystrophy to see if their muscles accept corrected cells and whether they multiply and take over the function of damaged cells.
As you read this text, you will surely use several muscles without being aware of them: Your heart muscle pumps blood through your arteries, your eye muscles let you follow the words in this sentence, and your hand muscles hold the tablet or cell phone. Muscles make up 40 percent of your body weight; we usually have 656 of them. Now imagine they are slowly losing their strength. No training, no protein shake can rebuild their function.
This is the reality for most people in Simone Spuler’s outpatient clinic at the Charité Hospital in Berlin, Germany: Almost all of her 2,500 patients have muscular dystrophy, a progressive illness striking mostly young people. Muscle decline leads to a wheelchair and, eventually, an early death due to a heart attack or the inability to breathe. In Germany alone, 300,000 people live with this illness, the youngest barely a year old. The CDC estimates that its most common form, Duchenne, affects 1 in every 3,500 to 6,000 male births each year in the United States.
The devastating progression of the disease is what motivates Spuler and her team of 25 scientists to find a cure. In 2019, they made a spectacular breakthrough: For the first time, they successfully used mRNA to introduce the CRISPR-Cas9 tool into human muscle stem cells to repair the dystrophy. “It’s really just one tiny molecule that doesn’t work properly,” Spuler explains.
CRISPR-Cas9 is a technology that lets scientists select and alter parts of the genome. It’s still comparatively new but has advanced quickly since its discovery in the early 2010s. “We now have the possibility to repair certain mutations with genetic editing,” Spuler says. “It’s pure magic.”
She projects a warm, motherly air and a professional calm that inspires trust from her patients. She needs these qualities because the 60-year-old neurologist has one of the toughest jobs in the world: All day long, patients with the incurable diagnosis of muscular dystrophy come to her clinic, and she watches them decline over the years. “Apart from physiotherapy, there is nothing we can recommend right now,” she says. That motivated her early in her career, when she met her first patients at the Max Planck Institute for Neurobiology near Munich in the 1990s. “I knew I had 30, 40 years to find something.”
She learned from the luminaries of her profession with postdocs at the University of California San Diego, Harvard and Johns Hopkins, before serving as a clinical fellow at the Mayo Clinic. In 2005, the Charité offered her the opportunity to establish a specialized clinic for myasthenia, or muscular weakness. An important influence on Spuler, she says, has been the French microbiologist Emmanuelle Charpentier, who received the Nobel Prize in 2020 along with Jennifer Doudna for their CRISPR research, and has worked in Berlin since 2015.
When CRISPR was first introduced, it was mainly used to cut through DNA. However, the cut can lead to undesired side effects. For the muscle stem cells, Spuler now uses a base editor to repair the damaged molecule with super fine scissors or tweezers.
“Apart from physiotherapy, there is nothing we can recommend right now,” Spuler says about her patients with limb-girdle muscular dystrophy.
Pablo Castagnola
Last year, she proved that the method works in mice. Injecting repaired cells into the rodents led to new muscle fibers and, in 2021 and 2022, she passed the first safety meetings with the Paul-Ehrlich Institute, which is responsible for approving human gene editing trials in Germany. She raised the nearly four million Euros needed to test the new method in the first clinical trial in humans with limb-girdle muscular dystrophy, beginning with one muscle that can easily be measured, such as the biceps.
This spring, Weber and his parents drove the 400 miles from Munich to Berlin. At Spuler’s lab, her team took a biopsy from muscles in his left arm. The first two steps – extraction and repair in a culture dish – went according to plan; Spuler was able to repair the mutation in Weber’s cells outside his body.
Next year, Weber will be the youngest participant when Spuler starts to test the method in a trial of five people “in vivo,” inside their bodies. This will be the real moment of truth: Will the participants’ muscles accept the corrected cells? Will the cells multiply and take over the function of damaged cells, just like Spuler was able to do in her lab with the rodents?
The effort is costly and complex. “The biggest challenge is to make absolutely sure that we don’t harm the patient,” Spuler says. This means scanning their entire genomes, “so we don’t accidentally damage or knock out an important gene.”
Weber, who asked not to be identified by his real name, is looking forward to the trial and he feels confident that “the risks are comparatively small because the method will only be applied to one muscle. The worst that can happen is that it doesn’t work. But in the best case, the muscle function will improve.”
He was so impressed with the Charité scientists that he decided to study biology at his university. He’s read extensively about CRISPR, so he understands why he has three healthy siblings. “That’s the statistics,” the biologist in training explains. “You get two sets of genes from each parent, and you have to get two faulty mutations to have muscular dystrophy. So we fit the statistics exactly: One of us four kids inherited the mutation.”
It was his mother, a college teacher, and father, a physicist by training, who heard about Spuler’s research. Even though Weber does not live at home anymore, having a chronically ill son is nearly a full-time job for his mother, Annette. The Berlin visit and the trial are financed separately through private sponsors, but the fights with Weber’s health insurance are frustrating and time-consuming. “Physiotherapy is the only thing that helps a bit,” Weber says, “and yet, they fought us on approving it every step of the way.”
Spuler does not want to evoke unrealistic expectations. “Patients who are wheelchair-bound won’t suddenly get up and walk."
Her son continues to exercise as much as possible. Riding his bicycle to the university has become too difficult, so he got an e-scooter. He had to give up competitive tennis because he does not have the stamina for a two-hour match, but he can still play with his dad or his buddies for an hour. His closest friends know about the diagnosis. “They help me, for instance, to lift something heavy because I can’t do that anymore,” Weber says.
The family was elated to find medical support at the Munich Muscle Center by the German Alliance for Muscular Patients and then at Spuler’s clinic in Berlin. “When you hear that this is a progressive illness with no chance of improvement, your world collapses as a parent,” Annette Weber says. “And then all of a sudden, there is this woman who sees scientific progress as an opportunity. Even just to be able to participate in the study is fantastic.”
Spuler does not want to evoke unrealistic expectations. “Patients who are wheelchair-bound won’t suddenly get up and walk,” she says. After all, she will start by applying the gene editor to only one muscle, “but it would be a big step if even a small muscle that is essential to grip something, or to swallow, regains function.”
Weber agrees. “I understand that I won’t regain 100 percent of my muscle function but even a small improvement or at least halting the deterioration is the goal.”
And yet, Spuler and others are ultimately searching for a true solution. In a separate effort, Massachusetts-based biotech company Sarepta announced this month it will seek expedited regulators’ approval to treat Duchenne patients with its investigational gene therapy. Unlike Spuler’s methods, Sarepta focuses specifically on the Duchenne form of muscular dystrophy, and it uses an adeno-assisted virus to deliver the therapy.
Spuler’s vision is to eventually apply gene editing to the entire body of her patients. To speed up the research, she and a colleague founded a private research company, Myopax. If she is able to prove that the body accepts the edited cells, the technique could be used for other monogenetic illnesses as well. “When we speak of genetic editing, many are scared and say, oh no, this is God’s work,” says Spuler. But she sees herself as a mechanic, not a divine being. “We really just exchange a molecule, that’s it.”
If everything goes well, Weber hopes that ten years from now, he will be the one taking biopsies from the next generation of patients and repairing their genes.
Artificial Intelligence is already helping to grow some of the food we eat.
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.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
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.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
