Body-on-a-chip, prime editing, and gut microbes all are poised to make a big impact in 2020.
1. Happening Now: Body-on-a-Chip Technology Is Enabling Safer Drug Trials and Better Cancer Research
Researchers have increasingly used the technology known as "lab-on-a-chip" or "organ-on-a-chip" to test the effects of pharmaceuticals, toxins, and chemicals on humans. Rather than testing on animals, which raises ethical concerns and can sometimes be inaccurate, and human-based clinical trials, which can be expensive and difficult to iterate, scientists turn to tiny, micro-engineered chips—about the size of a thumb drive.
It's possible that doctors could one day take individual cell samples and create personalized treatments, testing out any medications on the chip.
The chips are lined with living samples of human cells, which mimic the physiology and mechanical forces experienced by cells inside the human body, down to blood flow and breathing motions; the functions of organs ranging from kidneys and lungs to skin, eyes, and the blood-brain barrier.
A more recent—and potentially even more useful—development takes organ-on-a-chip technology to the next level by integrating several chips into a "body-on-a-chip." Since human organs don't work in isolation, seeing how they all react—and interact—once a foreign element has been introduced can be crucial to understanding how a certain treatment will or won't perform. Dr. Shyni Varghese, a MEDx investigator at the Duke University School of Medicine, is one of the researchers working with these systems in order to gain a more nuanced understanding of how multiple different organs react to the same stimuli.
Her lab is working on "tumor-on-a-chip" models, which can not only show the progression and treatment of cancer, but also model how other organs would react to immunotherapy and other drugs. "The effect of drugs on different organs can be tested to identify potential side effects," Varghese says. In addition, these models can help the researchers figure out how cancers grow and spread, as well as how to effectively encourage immune cells to move in and attack a tumor.
One body-on-a-chip used by Dr. Varghese's lab tracks the interactions of five organs—brain, heart, liver, muscle, and bone.
As their research progresses, Varghese and her team are looking for ways to maintain the long-term function of the engineered organs. In addition, she notes that this kind of research is not just useful for generalized testing; "organ-on-chip technologies allow patient-specific analyses, which can be used towards a fundamental understanding of disease progression," Varghese says. It's possible that doctors could one day take individual cell samples and create personalized treatments, testing out any medications on the chip for safety, efficacy, and potential side effects before writing a prescription.
2. Happening Soon: Prime Editing Will Have the Power to "Find and Replace" Disease-Causing Genes
Biochemist David Liu made industry-wide news last fall when he and his lab at MIT's Broad Institute, led by Andrew Anzalone, published a paper on prime editing: a new, more focused technology for editing genes. Prime editing is a descendant of the CRISPR-Cas9 system that researchers have been working with for years, and a cousin to Liu's previous innovation—base editing, which can make a limited number of changes to a single DNA letter at a time.
By contrast, prime editing has the potential to make much larger insertions and deletions; it also doesn't require the tweaked cells to divide in order to write the changes into the DNA, which could make it especially suitable for central nervous system diseases, like Parkinson's.
Crucially, the prime editing technique has a much higher efficiency rate than the older CRISPR system, and a much lower incidence of accidental insertions or deletions, which can make dangerous changes for a patient.
It also has a very broad potential range: according to Liu, 89% of the pathogenic mutations that have been collected in ClinVar (a public archive of human variations) could, in principle, be treated with prime editing—although he is careful to note that correcting a single genetic mutation may not be sufficient to fully treat a genetic disease.
Figuring out just how prime editing can be used most effectively and safely will be a long process, but it's already underway. The same day that Liu and his team posted their paper, they also made the basic prime editing constructs available for researchers around the world through Addgene, a plasmid repository, so that others in the scientific community can test out the technique for themselves. It might be years before human patients will see the results, and in the meantime, significant bioethical questions remain about the limits and sociological effects of such a powerful gene-editing tool. But in the long fight against genetic diseases, it's a huge step forward.
3. Happening When We Fund It: Focusing on Microbiome Health Could Help Us Tackle Social Inequality—And Vice Versa
The past decade has seen a growing awareness of the major role that the microbiome, the microbes present in our digestive tract, play in human health. Having a less-healthy microbiome is correlated with health risks like diabetes and depression, and interventions that target gut health, ranging from kombucha to fecal transplants, have cropped up with increasing frequency.
New research from the University of Maine's Dr. Suzanne Ishaq takes an even broader view, arguing that low-income and disadvantaged populations are less likely to have healthy, diverse gut bacteria, and that increasing access to beneficial microorganisms is an important juncture of social justice and public health.
"Basically, allowing people to lead healthy lives allows them to access and recruit microbes."
"Typically, having a more diverse bacterial community is associated with health, and having fewer different species is associated with illness and may leave you open to infection from bacteria that are good at exploiting opportunities," Ishaq says.
Having a healthy biome doesn't mean meeting one fixed ratio of gut bacteria, since different combinations of microbes can generate roughly similar results when they work in concert. Generally, "good" microbes are the ones that break down fiber and create the byproducts that we use for energy, or ones like lactic acid bacteria that work to make microbials and keep other bacteria in check. The microbial universe in your gut is chaotic, Ishaq says. "Microbes in your gut interact with each other, with you, with your food, or maybe they don't interact at all and pass right through you." Overall, it's tricky to name specific microbial communities that will make or break someone's health.
There are important corollaries between environment and biome health, though, which Ishaq points out: Living in urban environments reduces microbial exposure, and losing the microorganisms that humans typically source from soil and plants can reduce our adaptive immunity and ability to fight off conditions like allergies and asthma. Access to green space within cities can counteract those effects, but in the U.S. that access varies along income, education, and racial lines. Likewise, lower-income communities are more likely to live in food deserts or areas where the cheapest, most convenient food options are monotonous and low in fiber, further reducing microbial diversity.
Ishaq also suggests other areas that would benefit from further study, like the correlation between paid family leave, breastfeeding, and gut microbiota. There are technical and ethical challenges to direct experimentation with human populations—but that's not what Ishaq sees as the main impediment to future research.
"The biggest roadblock is money, and the solution is also money," she says. "Basically, allowing people to lead healthy lives allows them to access and recruit microbes."
That means investment in things we already understand to improve public health, like better education and healthcare, green space, and nutritious food. It also means funding ambitious, interdisciplinary research that will investigate the connections between urban infrastructure, housing policy, social equity, and the millions of microbes keeping us company day in and day out.
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.
