Antibody Testing Alone is Not the Key to Re-Opening Society
[Editor's Note: We asked experts from different specialties to weigh in on a timely Big Question: "How should immunity testing play a role in re-opening society?" Below, a virologist offers her perspective.]
With the advent of serology testing and increased emphasis on "re-opening" America, public health officials have begun considering whether or not people who have recovered from COVID-19 can safely re-enter the workplace.
"Immunity certificates cannot certify what is not known."
Conventional wisdom holds that people who have developed antibodies in response to infection with SARS-CoV-2, the coronavirus that causes COVID-19, are likely to be immune to reinfection.
For most acute viral infections, this is generally true. However, SARS-CoV-2 is a new pathogen, and there are currently many unanswered questions about immunity. Can recovered patients be reinfected or transmit the virus? Does symptom severity determine how protective responses will be after recovery? How long will protection last? Understanding these basic features is essential to phased re-opening of the government and economy for people who have recovered from COVID-19.
One mechanism that has been considered is issuing "immunity certificates" to individuals with antibodies against SARS-CoV-2. These certificates would verify that individuals have already recovered from COVID-19, and thus have antibodies in their blood that will protect them against reinfection, enabling them to safely return to work and participate in society. Although this sounds reasonable in theory, there are many practical reasons why this is not a wise policy decision to ease off restrictive stay-home orders and distancing practices.
Too Many Scientific Unknowns
Serology tests measure antibodies in the serum—the liquid component of blood, which is where the antibodies are located. In this case, serology tests measure antibodies that specifically bind to SARS-CoV-2 virus particles. Usually when a person is infected with a virus, they develop antibodies that can "recognize" that virus, so the presence of SARS-CoV-2 antibodies indicates that a person has been previously exposed to the virus. Broad serology testing is critical to knowing how many people have been infected with SARS-CoV-2, since testing capacity for the virus itself has been so low.
Tests for the virus measure amounts of SARS-CoV-2 RNA—the virus's genetic material—directly, and thus will not detect the virus once a person has recovered. Thus, the majority of people who were not severely ill and did not require hospitalization, or did not have direct contact with a confirmed case, will not test positive for the virus weeks after they have recovered and can only determine if they had COVID-19 by testing for antibodies.
In most cases, for most pathogens, antibodies are also neutralizing, meaning they bind to the virus and render it incapable of infecting cells, and this protects against future infections. Immunity certificates are based on the assumption that people with antibodies specific for SARS-CoV-2 will be protected against reinfection. The problem is that we've only known that SARS-CoV-2 existed for a little over four months. Although studies so far indicate that most (but not all) patients with confirmed COVID-19 cases develop antibodies, we don't know the extent to which antibodies are protective against reinfection, or how long that protection will last. Immunity certificates cannot certify what is not known.
The limited data so far is encouraging with regard to protective immunity. Most of the patient sera tested for antibodies show reasonable titers of IgG, the type of antibodies most likely to be neutralizing. Furthermore, studies have shown that these IgG antibodies are capable of neutralizing surrogate viruses as well as infectious SARS-CoV-2 in laboratory tests. In addition, rhesus monkeys that were experimentally infected with SARS-CoV-2 and allowed to recover were protected from reinfection after a subsequent experimental challenge. These data tentatively suggest that most people are likely to develop neutralizing IgG, and protective immunity, after being infected by SARS-CoV-2.
However, not all COVID-19 patients do produce high levels of antibodies specific for SARS-CoV-2. A small number of patients in one study had no detectable neutralizing IgG. There have also been reports of patients in South Korea testing PCR positive after a prior negative test, indicating reinfection or reactivation. These cases may be explained by the sensitivity of the PCR test, and no data have been produced to indicate that these cases are genuine reinfection or recurrence of viral infection.
Complicating matters further, not all serology tests measure antibody titers. Some rapid serology tests are designed to be binary—the test can either detect antibodies or not, but does not give information about the amount of antibodies circulating. Based on our current knowledge, we cannot be certain that merely having any level of detectable antibodies alone guarantees protection from reinfection, or from a subclinical reinfection that might not cause a second case of COVID-19, but could still result in transmission to others. These unknowns remain problematic even with tests that accurately detect the presence of antibodies—which is not a given today, as many of the newly available tests are reportedly unreliable.
A Logistical and Ethical Quagmire
While most people are eager to cast off the isolation of physical distancing and resume their normal lives, mere desire to return to normality is not an indicator of whether those antibodies actually work, and no certificate can confer immune protection. Furthermore, immunity certificates could lead to some complicated logistical and ethical issues. If antibodies do not guarantee protective immunity, certifying that they do could give antibody-positive people a false sense of security, causing them to relax infection control practices such as distancing and hand hygiene.
"We should not, however, place our faith in assumptions and make return to normality contingent on an arbitrary and uninformative piece of paper."
Certificates could be forged, putting susceptible people at higher exposure risk. It's not clear who would issue them, what they would entitle the bearer to do or not do, or how certification would be verified or enforced. There are many ways in which such certificates could be used as a pretext to discriminate against people based on health status, in addition to disability, race, and socioeconomic status. Tracking people based on immune status raises further concerns about privacy and civil rights.
Rather than issuing documents confirming immune status, we should instead "re-open" society cautiously, with widespread virus and serology testing to accurately identify and isolate infected cases rapidly, with immediate contact tracing to safely quarantine and monitor those at exposure risk. Broad serosurveillance must be coupled with functional assays for neutralization activity to begin assessing how protective antibodies might actually be against SARS-CoV-2 infection. To understand how long immunity lasts, we should study antibodies, as well as the functional capabilities of other components of the larger immune system, such as T cells, in recovered COVID-19 patients over time.
We should not, however, place our faith in assumptions and make return to normality contingent on an arbitrary and uninformative piece of paper. Re-opening society, the government, and the economy depends not only on accurately determining how many people have antibodies to SARS-CoV-2, but on a deeper understanding of how those antibodies work to provide protection.
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.