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  • Research team identifies carbonate-dissolving microorganisms

    Research team identifies carbonate-dissolving microorganisms

    A University of Nebraska-Lincoln research team has identified new microscopic players in the global carbon cycle, a discovery that paints a clearer picture of carbon flow through the environment and provides key information for the sustainable development of bioenergy sources.

    A recent publication in Communications Earth and Environment is among the first studies to show that methanogens — microorganisms ubiquitous in low-oxygen environments like lakes, wetlands, aquifers, freshwater habitats, a variety of soils and even permafrost — can propel their growth by consuming hydrogen and dissolving calcium carbonate, one of Earth’s most abundant minerals. This metabolic process produces methane, which is both a biofuel and a potent greenhouse gas.

    “This is one of the first demonstrations of microbial dissolution of calcium carbonate at a higher pH,” said Karrie Weber, professor of biological sciences and Earth and atmospheric sciences.

    Weber and Nicole Fiore, a lecturer and former graduate student in biological sciences, led the Husker team. The paper culminates more than a decade of work at the university, including contributions from many graduate and undergraduate students, and postdoctoral researchers.

    By pinpointing the carbonate-dissolving microorganisms, the team has challenged the prevailing belief that carbonate minerals — which contain roughly 80% of the Earth’s carbon — are stable at elevated pH levels. This potential instability means that in certain locations — where there is subsurface carbon sequestered as carbonates and conditions that support microbial life — the sequestered carbon may be converted to methane, especially in underground hydrogen energy reservoirs.

    “Something to consider, when looking at these kinds of strategies, is whether there are methanogens present and the extent to which they might be able to undo what we are doing with mineral sequestration,” said Fiore, whose National Science Foundation Graduate Research Fellowship funded a portion of the work.

    The work started with a mud sample from alkaline saline wetland soil in Lincoln. The researchers already knew that any methanogens in the sample would consume hydrogen — but they did not know if the microbes could dissolve calcium carbonate to generate methane. To answer this question, they created special culture conditions that included hydrogen and calcium carbonate, an environment designed to weed out microorganisms not capable of using the carbonate.

    A small community of microorganisms survived the culture process, the genomes of which the researchers constructed using a technique called genome-resolved metagenomics. The community contained not only methanogens, but five types of bacteria. The team was able to visualize these microbes using Nebraska’s CARS (coherent anti-Stokes Raman scattering) microscope, part of the Laser Assisted Nano Engineering Lab. The work demonstrated that the microbes were attached to the carbonate mineral’s surface.

    In a departure from other studies exploring this question, the Husker team strictly controlled the pH level — the acidity or basicity — of the culture. This is because carbonate minerals change state when pH levels fluctuate. By holding pH steady, the team ensured that mineral dissolution could be more definitively attributed to the microorganisms’ metabolism rather than the culture’s shifting chemistry.

    Methanogens’ metabolism has consequences in the bioenergy realm. Researchers are increasingly interested in using natural hydrogen as a clean fuel — Nebraska, in fact, is home to the United States’ first well drilled to find naturally occurring hydrogen. Weber said it is important to determine the extent to which microbial processes may impact subsurface hydrogen reservoirs. Another avenue to explore is whether the methane that methanogens produce can be harnessed as an alternative natural gas, in addition to subsurface hydrogen.

    The next step, Weber said, is determining which other carbonate materials methanogens are capable of dissolving and searching for biosignatures that confirm the dissolution process is happening in the environment — not just in the laboratory. The team believes it is likely occurring worldwide, as both carbonates and the same type of methanogens are side by side in many locations across the globe.

    “This is local research with global significance,” Weber said.

    In addition to Fiore’s NSF grant, the work was supported by a NASA Nebraska Space Grant and funding from the Nebraska Center for Energy Sciences Research and the NSF-funded Center for Root and Rhizobiome Innovation.

    Other Husker authors are Xi Huang, research assistant professor in electrical and computer engineering; Yongfeng Lu, Lott Distinguished Professor of electrical and computer engineering; Nicole Buan, professor of biochemistry; Dan Miller, adjunct faculty in agronomy and horticulture and a Lincoln-based research microbiologist with the U.S. Department of Agriculture; former postdoctoral researcher Sanjay Antony-Babu; and former students Anthony Kohtz, Donald Pan and Caitlin Lahey.

  • AI unlocks genetic clues to personalize cancer treatment

    AI unlocks genetic clues to personalize cancer treatment

    A groundbreaking study led by USC Assistant Professor of Computer Science Ruishan Liu has uncovered how specific genetic mutations influence cancer treatment outcomes — insights that could help doctors tailor treatments more effectively. The largest study of its kind, the research analyzed data for more than 78,000 cancer patients across 20 cancer types. Patients received immunotherapies, chemotherapies and targeted therapies.

    Using advanced computational analysis, the researchers identified nearly 800 genetic changes that directly impacted survival outcomes. They also discovered 95 genes significantly associated with survival in cancers such as breast, ovarian, skin, and gastrointestinal cancers.

    Building on these insights, the team developed a machine learning tool to predict how patients with advanced lung cancer might respond to immunotherapy.

    “These discoveries highlight how genetic profiling can play a crucial role in personalizing cancer care,” said Liu. “By understanding how different mutations influence treatment response, doctors can select the most effective therapies — potentially avoiding ineffective therapies and focusing on those most likely to help.”

    Published in Nature Communications, the study highlights the critical roles of genes such as TP53, CDKN2A, and CDKN2B in influencing treatment outcomes, validating these associations with real-world data.

    Study co-authors are Shemra Rizzo, Lisa Wang, Nayan Chaudhary, Sophia Maund, and Sarah McGough and Ryan Copping of Genentech; Marius Rene Garmhausen of Roche; and James Zou of Stanford University.

    Why do mutations matter?

    Genetic mutations — changes in DNA — can influence how cancer develops and how a patient responds to treatment. Some mutations occur randomly, while others are inherited.

    In cancer, mutations can determine whether a tumor is more aggressive or how it might respond to certain treatments. Today, genetic testing is increasingly used in cancer care to identify these mutations, allowing doctors select treatments more precisely.

    For example, Patients diagnosed with non-small cell lung cancer (NSCLC) often receive genomic testing for mutations in genes like KRAS, EGFR and ALK to determine whether targeted therapies or immunotherapies might be effective.

    Key findings from the study include:

    • KRAS mutations in advanced non-small cell lung cancer were linked to poorer response to a common treatment (EGFR inhibitors), suggesting alternative treatments may be needed. • NF1 mutations improved responses to immunotherapy and worsened responses to certain targeted therapies, highlighting their complex role in treatment. • PI3K pathway mutations, which regulate cell growth, had varying effects depending on cancer type, with different responses in breast, melanoma and renal cancers. • DNA repair pathway mutations improved immunotherapy effectiveness in lung cancer by increasing tumor instability. • Mutations in immune-related pathways were associated with better survival rates for lung cancer patients treated with immunotherapy, suggesting not all mutations hinder treatment success.

    A powerful predictive tool

    While cancer treatments have traditionally followed a one-size-fits-all approach, where patients with the same type of cancer receive the same standard therapies, the study underscores the importance of precision medicine, which tailors treatment based on a patient’s unique genetic makeup.

    Yet while vast amounts of mutation data exist, only a small number have clinically validated treatments, limiting potential real-world impact and patient benefit. To bridge this gap, based on their findings, Liu’s team used machine learning to analyze how multiple mutations interact to influence treatment outcomes.

    “Our goal was to find patterns that might not be obvious at first glance.” Ruishan Liu

    They developed a Random Survival Forest (RSF) model, a predictive tool designed to refine treatment recommendations for lung cancer patients. By integrating large-scale real-world data with machine learning, the model identified new mutation-treatment interactions.

    “Our goal was to find patterns that might not be obvious at first glance, and then translate these insights into real-world tools that can expand access to immunotherapy for people with cancer,” Lui said. “One key innovation lies in integrating huge amounts of data with advanced statistical and machine learning techniques to uncover previously unrecognized mutation-treatment interactions.”

    While further clinical trials are needed, Liu sees this study as an important step toward making cancer treatment more precise and personalized.

    “This research shows the power of computational science in transforming complex clinical and genomic data into actionable insights,” she said. “It’s deeply fulfilling to contribute to tools and knowledge that can directly improve patient care.”

  • Novel ‘living’ biomaterial aims to advance regenerative medicine

    Novel ‘living’ biomaterial aims to advance regenerative medicine

    A biomaterial that can mimic certain behaviors within biological tissues could advance regenerative medicine, disease modeling, soft robotics and more, according to researchers at Penn State.

    Materials created up to this point to mimic tissues and extracellular matrices (ECMs) — the body’s biological scaffolding of proteins and molecules that surrounds and supports tissues and cells — have all had limitations that hamper their practical applications, according to the team. To overcome some of those limitations, the researchers developed a bio-based, “living” material that encompasses self-healing properties and mimics the biological response of ECMs to mechanical stress.

    They published their results in Materials Horizons, where the research was also featured on the cover of the journal.

    “We developed a cell-free — or acellular — material that dynamically mimics the behavior of ECMs, which are key building blocks of mammalian tissues that are crucial for tissue structure and cell functions,” said corresponding author Amir Sheikhi, associate professor of chemical engineering and the Dorothy Foehr Huck and J. Lloyd Huck Early Career Chair in Biomaterials and Regenerative Engineering.

    According to the researchers, previous iterations of their material — a hydrogel, or water-rich polymer network — were synthetic and lacked the desired combination of mechanical responsiveness and biological mimicry of ECMs.

    “Specifically, these materials need to replicate nonlinear strain-stiffening, which is when ECM networks stiffen under strain caused by physical forces exerted by cells or external stimuli,” Sheikhi said, explaining nonlinear strain-stiffening is important for providing structural support and facilitating cell signaling. “The materials also need to replicate the self-healing properties necessary for tissue structure and survival. Prior synthetic hydrogels had difficulties in balancing material complexity, biocompatibility and mechanical mimicry of ECMs.”

    The team addressed these limitations by developing acellular nanocomposite living hydrogels (LivGels) made from “hairy” nanoparticles. The nanoparticles are composed of nanocrystals, or “nLinkers,” with disordered cellulose chains, or “hairs,” at the ends. These hairs introduce anisotropy, meaning the nLinkers have different properties depending on their directional orientation and allow dynamic bonding with biopolymer networks. In this case, the nanoparticles bonded with a biopolymeric matrix of modified alginate, which is a natural polysaccharide found in brown algae.

    “These nLinkers form dynamic bonds within the matrix that enable strain-stiffening behavior, that is, mimicking ECM’s response to mechanical stress; and self-healing properties, which restore integrity after damage,” Sheikhi said, noting that the researchers used rheological testing, which measures how material behaves under various stressors, to measure how rapidly the LivGels recovered their structure after high strain. “This design approach allowed fine-tuning of the material’s mechanical properties to match those of natural ECMs.”

    Critically, Sheikhi said, this material is entirely made of biological materials and avoids synthetic polymers with potential biocompatibility issues. Beyond mitigating the limitations of previously developed materials, LivGels achieve the dual traits of nonlinear mechanics and self-healing without sacrificing structural integrity. The nLinkers specifically facilitate dynamic interactions that allow precise control of stiffness and strain-stiffening properties. Taken together, the design approach converts bulk, static hydrogels to dynamic hydrogels that closely mimic ECMs.

    The potential applications include scaffolding for tissue repair and regeneration within regenerative medicine, simulating tissue behavior for drug testing and creating realistic environments for studying disease progression. The researchers said it could also be used for 3D bioprinting customizable hydrogels or for developing soft robotics with adaptable mechanical properties.

    “Our next steps include optimizing LivGels for specific tissue types, exploring in vivo applications for regenerative medicine, integrating LivGels with 3D bioprinting platforms and investigating potential in dynamic wearable or implantable devices,” Sheikhi said.

    Roya Koshani, a chemical engineering post-doctoral scholar at Penn State, and Sina Kheirabadi, a doctoral candidate in chemical engineering at Penn State, were co-authors on the paper. Sheikhi is also affiliated with the Departments of Biomedical Engineering, of Chemistry and of Neurosurgery, and with the Huck Institutes of the Life Sciences.

    Support for this research was provided by Penn State, including from: the Dorothy Foehr Huck and J. Lloyd Huck Early Career Chair; the Convergence Center for Living Multifunctional Material Systems and the Cluster of Excellence Living, Adaptive and Energy-autonomous Materials Systems Living Multifunctional Materials Collaborative Research Seed Grant Program; the Materials Research Institute; and the College of Engineering Materials Matter at the Human Level seed grants.

  • High-yield rice breed emits up to 70% less methane

    High-yield rice breed emits up to 70% less methane

    Rice cultivation is responsible for around 12% of global methane emissions, and these emissions are expected to increase with global warming and as the human population continues to grow. Now, scientists have identified chemical compounds released by rice roots that determine how much methane the plants emit. On February 3 in the Cell Press journal Molecular Plant, they report that this information enabled them to breed a new strain of rice that emits up to 70% less methane.

    “This study shows that you can have low methane and still have a rice with high yields,” says senior author Anna Schnürer, a microbiologist at the Swedish University of Agricultural Sciences. “And you can do it using traditional breeding methods, without GMO, if you know what you’re looking for.”

    The methane emitted by rice paddies is produced by microbes that break down organic compounds released from rice plant roots. Rice and other plants release these compounds, known as “root exudates,” to feed soil microbes, which in turn aid plant growth by releasing nutrients that the plants can absorb. Though it’s been long understood that soil microbes and root exudates are involved in methane emissions, it was unclear which chemical compounds in root exudates were responsible.

    To identify which root exudate compounds are converted into methane, the researchers compared root exudates from two different rice varieties — SUSIBA2, a low-methane emitting GMO variety, and Nipponbare, a non-GMO cultivar with average methane emissions. They found that SUSIBA2 roots produced significantly less fumarate and noted a correlation between the amount of fumarate secreted and the abundance of methane-releasing archaea or “methanogens” in the surrounding soil.

    To confirm fumarate’s role, the researchers added fumarate to the soil of rice plants grown in containers, which resulted in increased methane emissions. They also demonstrated that applying oxantel, a chemical that inhibits the enzymatic breakdown of fumarate, effectively reduced methane emissions. However, since the SUSIBA2 plants still produced less methane than the Nipponbare plants, the researchers realized that fumarate isn’t the only piece to the puzzle.

    “It was almost like having a riddle,” says Schnürer. “We noticed that the soil itself contained something that reduced methane emissions, so we started thinking that there must be an inhibitor of some kind that is also causing the difference between the varieties.”

    When they re-analyzed the root exudates, the team noticed that SUSIBA2 plants also release significantly more ethanol. Adding ethanol to the soil surrounding rice plants reduced methane emissions.

    Next, the team investigated whether they could use traditional breeding methods to produce low methane-emitting rice with high yield. To do this, they crossbred a high yield or “elite” rice variety with a previously identified low-methane-emitting variety (the Heijing cultivar) whose root exudate was low in fumarate and high in ethanol.

    The rice plants from this crossbreeding consistently released root exudates with low fumarate and high ethanol (LFHE) profiles. When the researchers grew these LFHE rice varieties at various field sites throughout China, they showed that the LFHE rice produced 70% less methane on average compared with the elite variety from which it was bred. The LFHE crops also produced relatively high yields — 8.96 tons/hectare on average, compared to the 2024 global average of 4.71 tons/hectare.

    The researchers also investigated whether ethanol and oxantel could be used to reduce methane emissions at a large scale. Based on a two-year field trial at two different sites in China, this treatment resulted in approximately 60% reduction in methane emissions without impacting crop yield.

    Now, the researchers are working to register the LFHE rice as a variety with the Chinese government and others, meaning that it could be marketed to farmers in the future. They’re also working with fertilizer companies to investigate whether oxantel could be added to commercial fertilizers.

    “To make these things happen, we’ll also need some encouragement from governments to motivate and support farmers to use these low-methane varieties,” says Schnürer. “It’s one thing to breed eco-friendly rice varieties, but then it’s critical to get them on the market and to get the farmers to accept them.”

    This research was supported by Mr. Zheng Fang, Beijing Xianhe Transportation Technology Co. Ltd., Trees and Crops for the Future, the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, NSFC Projects of International Cooperation and Exchanges, and the China Scholarship Council.

  • New ways to modulate cell activity remotely

    New ways to modulate cell activity remotely

    Imagine being at a big marquee event at an arena, like the Super Bowl, with the roar of the crowd, the smell of hot dogs, and a sea of jerseys all merging into one chaotic blur. While the frenzied, exciting environment certainly enhances your viewing experience, it can also make it difficult to find the people you came with if you get separated. If you’re communicating by phone or waving from the stands, it can be an exhausting game of hide-and-seek amid the noise and commotion.

    Now imagine if you had a way to remotely guide them to you with pinpoint precision — an app that highlighted their exact location and gently nudged them to move in the right direction. That, in essence, is what bioengineer Lukasz Bugaj and his team at the University of Pennsylvania have achieved — except the arena is the human body, and the people you are directing are engineered cells sent in to carry out specific tasks like killing cancer or repairing damaged tissue.

    “There’s a lot going on in complex living systems, so when we send modified cells into the body to execute a particular function, like to go in and find pathogens or cancerous cells, wouldn’t it be great if we could communicate with them, guide them, make sure they’re going exactly where they need to go, at the right time, to do the right thing?” asks Bugaj.

    In a new paper, the Bugaj Lab introduces tools that essentially, “remotely and non-invasively communicate with and control the activity of cells” once they’ve entered the arena. Published in Nature Methods, the paper focuses on a protein the team developed called Melt, which can be toggled by temperature.

    From light to heat: developing Melt

    Controlling cellular behavior with light — a field known as optogenetics — has been a game-changer in biology since its development almost two decades ago. It involves researchers using light-sensitive proteins to activate or deactivate specific pathways within cells. But there’s a catch, light doesn’t penetrate deeply into tissues, making it impractical for many therapeutic applications.

    “We needed something that could go deeper,” says Bugaj. “That’s where temperature comes in. Heat is a more penetrant stimulus — it travels through tissues in ways visible light simply can’t.”

    The breakthrough came from a surprising source, a fungus known as Botrytis cinerea, infamous for causing rot in strawberries and grapes. The fungus produces a protein called BcLOV4, which was initially studied for its light sensitivity, but when Bugaj’s lab introduced the protein into human cell lines, something unexpected happened.

    “We noticed that the protein wasn’t just responding to light — it was responding to temperature,” says first author and former Ph.D. student in the Bugaj Lab, Will Benman. “That’s when we thought, ‘okay, that’s actually really, really exciting,’ because there are a lot of known proteins that respond to light, but not as many proteins that respond to temperature.”

    The team wondered, could they engineer the protein to respond solely to temperature? And if so, could they use it to control cellular behavior in a non-invasive way? Over months of experiments, they modified BcLOV4 into a new protein that acts as a purely temperature-sensitive tool: Melt — short for Membrane Localization using Temperature.

    “We broke its light sensitivity and tuned its temperature sensitivity to operate at human body temperatures,” says Pavan Iyengar, a former undergraduate researcher in the Bugaj Lab. “Now we have a switch that works like a dimmer — you raise the temperature, and it activates; lower it, and it deactivates.”

    By fusing Melt to different cellular pathways, the team demonstrated precise control over processes like cell signaling, peptide breakdown, and even cell death. In one striking experiment, they showed that applying a device for topical cooling — a “glorified icepack” — to an animal model could trigger cancer cell death, without the systemic toxicity of traditional chemotherapy.

    Melt in action

    The team also explored Melt’s use in basic research, where controlling cellular pathways in real-time can reveal new insights into how cells function.

    “It’s kind of a rare case where a protein can do so many different things,” says co-first author of the Melt paper and a Ph.D. student in the Bugaj Lab, Zikang (Dennis) Huang. “It can sense light, it can sense temperature, it can go to the membrane, and also has some other molecular functions as well, whereas most known natural proteins may have only one of these functions. Once we figure out how this works, we can potentially design more new proteins that have those integrated functions in just a single protein.”

    In the near term, that the team believes Melt could aid in key discoveries in cancer therapies, enabling treatments that are more targeted and less toxic.

    “This work was only possible because of funding from the federal government and also through pilot funds from the Center for Precision Engineering for Health at Penn. Building from this initial support and our early results, our lab recently won a large NIH grant to further develop and test the efficacy of temperature-controlled tumor cell ablation in physiological models of cancer,” says Bugaj. “Further down the line, these tools could pave the way for new types of cell therapies that respond to physiological cues like fever or inflammation.”

  • Transforming longevity research: AI paves the way for personalized treatments in aging science

    Transforming longevity research: AI paves the way for personalized treatments in aging science

    A collaborative study between researchers from the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), and the Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Germany, investigated how advanced AI tools, like Large Language Models (LLMs), can make it easier to evaluate interventions for ageing and provide personalised recommendations. The findings were published in the leading review journal Ageing Research Reviews.

    Research into ageing is producing an overwhelming amount of data, making it difficult to determine which interventions — such as new medicines, dietary changes, or exercise routines — are safe and effective. This study investigated how AI can analyse data more efficiently and accurately, by proposing a comprehensive set of standards for AI systems to ensure they deliver accurate, reliable, and understandable evaluations through their ability to analyse complex biological data.

    The researchers identified eight critical requirements for effective AI-based evaluations:

    1. Correctness of the evaluation results. Data quality will be assessed for accuracy.
    2. Usefulness and comprehensiveness.
    3. Interpretability and explainability of the evaluation results. Clarity and conciseness of the results and the given explanations.
    4. Specific consideration of causal mechanisms affected by the intervention.
    5. Consideration of data in a holistic context:
      1. Efficacy and toxicity, and evidence for the existence of a large therapeutic window;
      2. Analyses in an “interdisciplinary” setting.
    6. Enabling reproducibility, standardisation, and harmonisation of the analyses (and of the reporting).
    7. Specific emphasis on diverse longitudinal large-scale data.
    8. Specific emphasis on results that relate to known mechanisms of ageing.

    Telling LLMs about these requirements as part of the prompting improved the quality of the recommendations they produced.

    Professor Brian Kennedy from the Department of Biochemistry & Physiology, and Healthy Longevity Translational Research Programme at NUS Medicine, who co-led the study, said, “We tested AI methods using real-world examples such as medicines and dietary supplements. We found that by following specific guidelines, AI can provide more accurate and detailed insights. For instance, when analysing rapamycin, a drug often studied for its potential to promote healthy ageing, the AI not only evaluated its efficacy but also provided context-specific explanations and caveats, such as possible side effects.”

    “The study’s findings could have far-reaching effects,” added Professor Georg Fuellen, Director, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, who co-led the study, “For healthcare, telling the AI about the critical requirements of a good response can enable it to find more effective treatments and make them safer to use. Generally, AI tools could design better clinical trials and help tailor health recommendations to each person. This research is a major step toward using AI to improve health outcomes for everyone, especially as they age.”

    Moving forward, the team is now focusing on a large-scale study of how to best prompt AI models for longevity-related intervention advice, to evaluate their accuracy and reliability for a wide array of carefully designed benchmarks, that is, curated, high-quality data. The validation of such AI systems is specifically important because the longevity interventions may then be implemented by a large number of healthy people. Prospective studies will need to demonstrate that AI-based evaluations can accurately predict successful outcomes in human trials, paving the way for safer and more effective health interventions.

    The team hopes to use their findings to make health and longevity interventions more precise and accessible, and ultimately improve the quality and duration of life. Collaboration between researchers, clinicians, and policymakers will be essential to establish robust regulatory frameworks, ensuring the safe and effective use of AI-driven evaluations.

  • Protein labeling of tens of millions of densely packed cells in organ-scale tissues

    Protein labeling of tens of millions of densely packed cells in organ-scale tissues

    A new technology developed at MIT enables scientists to label proteins across millions of individual cells in fully intact 3D tissues with unprecedented speed, uniformity, and versatility. Using the technology, the team was able to richly label whole rodent brains and other large tissue samples in a single day. In their new study in Nature Biotechnology, they also demonstrate that the ability to label proteins with antibodies at the single-cell level across whole brains can reveal insights left hidden by other widely used labeling methods.

    Profiling the proteins that cells are making is a staple of studies in biology, neuroscience and related fields because the proteins a cell is expressing at a given moment can reflect the functions the cell is trying to perform or its response to its circumstances, such as disease or treatment. As much as microscopy and labeling technologies have advanced, enabling innumerable discoveries, scientists have still lacked a reliable and practical way of tracking protein expression at the level of millions of densely packed individual cells in whole, 3D intact tissues such as an entire mouse brain or a full region of a human brain. Often confined to thin tissue sections under slides, scientists therefore haven’t had tools to thoroughly appreciate cellular protein expression in the whole, connected systems in which it occurs.

    “Conventionally, investigating the molecules within cells requires dissociating tissue into single cells or slicing it into thin sections, as light and chemicals required for analysis cannot penetrate deep into tissues. Our lab developed technologies such as CLARITY and SHIELD, which enable investigation of whole organs by rendering them transparent, but we now needed a way to chemically label whole organs to gain useful scientific insights” said study senior author Kwanghun Chung, associate professor in The Picower Institute for Learning and Memory, the Departments of Chemical Engineering and Brain and Cognitive Sciences, and the Institute for Medical Engineering and Science at MIT. “Imagine marinating a thick steak by simply dipping it in sauce. The outer layers absorb the marinade quickly and intensely, while the inner layers remain largely untouched unless the meat is soaked for an extended period. The same principle applies to chemical processing of tissues: if cells within a tissue are not uniformly processed, they cannot be quantitatively compared. The challenge is even greater for protein labeling, as the chemicals we use for labeling are hundreds of times larger than those in marinades. As a result, it can take weeks for these molecules to diffuse into intact organs, making uniform chemical processing of organ-scale tissues virtually impossible and extremely slow.”

    The new approach, called “CuRVE,” represents a major advance — years in the making — toward that goal by demonstrating a fundamentally new approach to uniformly processing large and dense tissues whole. In the study, the researchers explain how they overcame the technical barriers via an implementation of CuRVE called “eFLASH,” and provide copious vivid demonstrations of the technology, including how it yielded new neuroscience insights.

    “This is a significant leap, especially in terms of the actual performance of the technology,” said co-lead author Dae Hee Yun, a former MIT graduate student and now a senior application engineer at LifeCanvas Technologies, a startup company Chung founded to disseminate the tools his lab invents. The paper’s other lead author is Young-Gyun Park, a former MIT postdoctoral researcher now an assistant professor at KAIST in South Korea.

    Clever chemistry

    The fundamental reason why large, 3D tissue samples are hard to label uniformly is that antibodies seep into tissue very slowly, but are quick to bind to their target proteins. The practical effect of this speed mismatch is that simply soaking a brain in a bath of antibodies will mean that proteins are intensely well labeled on the outer edge of the tissue, but virtually none of the antibodies will find cells and proteins deeper inside.

    To improve labeling, the team conceived of a way — the conceptual essence of CuRVE — to resolve the speed mismatch. The strategy was to continuously control the pace of antibody binding while at the same time speeding up antibody permeation throughout the tissue. To figure out how this could work and to optimize the approach, they built and ran a sophisticated computational simulation that enabled them to test different settings and parameters, including different binding rates and tissue densities and compositions.

    Then they set out to implement their approach in real tissues. Their starting point was a previous technology, called “SWITCH,” in which Chung’s lab devised a way of temporarily turning off antibody binding, letting the antibodies permeate the tissue, and then turning binding back on. As well as it worked, Yun said, the team realized there could be substantial improvements if antibody binding speed could be controlled constantly, but the chemicals used in SWITCH were too harsh for such ongoing treatment. So the team screened a library of similar chemicals to find one that could more subtly and continuously throttle antibody binding speed. They found that deoxycholic acid was an ideal candidate. Using that chemical, the team could not only modulate antibody binding by varying the chemical’s concentration, but also by varying the labeling bath’s PH (or acidity).

    Meanwhile, to speed up antibody movement through tissues, the team used another prior technology invented in the Chung Lab: stochastic electrotransport. That technology accelerates the dispersion of antibodies through tissue by applying electric fields.

    Implementing this eFLASH system of accelerated dispersion with continuously modifiable binding speed produced the wide variety of labeling successes demonstrated in the paper. In all, the team reported using more than 60 different antibodies to label proteins in cells across whole brains of mice and rats; whole mouse embryos; other whole mouse organs including lung and heart; and blocks of brain tissue from larger animals including humans.

    Notably, each of these specimens were labeled within a day, an “ultra-fast” speed for whole, intact organs, the authors said. Moreover, different preparations did not require new optimization steps.

    Valuable visualizations

    Among the ways the team put eFLASH to the test was by comparing their labeling to another often-used method: genetically engineering cells to fluoresce when the gene for a protein of interest is being transcribed. The genetic method doesn’t require dispersing antibodies throughout tissue, but it can be prone to discrepancies because reporting gene transcription and actual protein production are not exactly the same thing. Yun added that while antibody labeling reliably and immediately reports on the presence of a target protein, the genetic method can be much less immediate and persistent, still fluorescing even when the actual protein is no longer present.

    In the study the team employed both kinds of labeling simultaneously in samples. Visualizing the labels that way, they saw many examples in which antibody labeling and genetic labeling differed widely. In some areas of mouse brains, they found that two-thirds of the neurons expressing PV (a protein prominent in certain inhibitory neurons) according to antibody labeling, did not show any genetically-based fluorescence. In another example, only a tiny fraction of cells that reported expression via the genetic method of a protein called ChAT also reported it via antibody labeling. In other words, there were cases where genetic labeling both severely underreported or overreported protein expression compared to antibody labeling.

    The researchers don’t mean to impugn the clear value of using the genetic reporting methods, but instead suggest that also using organ-wide antibody labeling, as eFLASH allows, can help put that data in a richer, more complete context. For instance, given that they saw cases of significant over-reporting of PV expression in mice (and significant variation among individual mice), researchers who use whole-brain antibody labeling of PV could gain a deeper basis for analyzing genetically indicated changes in the protein.

    “Our discovery of large regionalized loss of PV-immunoreactive neurons in healthy adult mice and with high individual variability emphasizes the importance of holistic and unbiased phenotyping,” the authors wrote.

    Or as Yun put it, the two different kinds of labeling are, “two different tools for the job.”

    In addition to Yun, Park and Chung, the paper’s other authors are Jae Hun Cho, Lee Kamentsky, Nicholas Evans, Nicholas DiNapoli, Katherine Xie, Seo Woo Choi, Alexandre Albanese, Yuxuan Tian, Chang Ho Sohn, Qiangge Zhang, Minyoung Kim, Justin Swaney, Webster Guan, Juhyuk Park, Gabi Drummond, Heejin Choi, Luzdary Ruelas, and Guoping Feng.

    Funding for the study came from the Burroughs Wellcome Fund, the Searle Scholars Program, a Packard Award in Science and Engineering, a NARSAD Young Investigator Award, the McKnight Foundation, the Freedom Together Foundation, The Picower Institute for Learning and Memory, the NCSOFT Cultural Foundation and the National Institutes of Health.

  • Scientists predict what will be top of the crops in UK by 2080 due to climate change

    Scientists predict what will be top of the crops in UK by 2080 due to climate change

    While climate change is likely to present significant challenges to agriculture in coming decades, it could also mean that crops such as chickpeas, soyabeans and oranges are widely grown across the UK, and home-produced hummus, tofu and marmalade are a common sight on our supermarket shelves by 2080.

    A new study led by the UK Centre for Ecology & Hydrology (UKCEH) in collaboration with the University of East Anglia (UEA) predicts that future warmer temperatures in this country would be suitable for a variety of produce such as oranges, chickpeas and okra that are traditionally grown in warmer parts of the world.

    Scientists investigated the future suitability for over 160 existing and new food crops in different regions of the UK under warming scenarios of 2 and 4 degrees Celsius compared to pre-industrial times.

    The modelling study, part of the OpenCLIM research project, is the most comprehensive research of its kind to date and is also the first to show how suitability for certain produce will vary across the UK. It provides mapped projections for every 1km square in the UK, giving valuable information to the farming and food sectors on the future opportunities and challenges of cultivating new crops here.

    Need for resilience

    Lead author Dr John Redhead, a Spatial Ecologist at UKCEH, said: “Our climate is expected to change substantially over coming decades at a time when there will be rising demand for food due to population growth. It is therefore essential that arable farming becomes more resilient; one possible solution is growing different crops that are more suited to the new local conditions.”

    Climate change is already having a major impact on UK agriculture, either affecting crop plants or the ability of farmers to manage them effectively. There have been several years of record low yields caused by extreme weather, often in combination, such as a wet winter followed by a particularly dry spring, while new agricultural pests and diseases are becoming established or increasing with climate change.

    Winners and losers

    Many new crops highlighted in the UKCEH-led study — funded by UK Research & Innovation (UKRI) and published in the journal Climate Resilience and Sustainability – are not only more tolerant of hotter, drier summers but also benefit from milder, wetter winters.

    The research looked at whether future climate scenarios would be more or less suitable for crops, compared with recent average UK temperatures, estimated at being around 0.5 degrees Celsius above pre-industrial times. The key findings were:

    • Climate change results in increased suitability for many current and potential new crops across much of the UK.
    • However, some key produce is likely to become harder to grow in the South East and East Anglia, the UK’s most productive arable regions. The climate in these areas is expected to become less suitable for wheat and strawberries under the 2 degrees of warming scenario, while 4 degrees of warming would result in reduced suitably for other major crops including onions and oats.
    • There would be substantial increases in suitability for a broad range of crops not currently widely grown in the UK — such as sunflower, durum wheat, soybeans, cow peas, chickpeas, citrus fruit and okra — as well as wine grapes.

    Diversity offers multiple benefits

    The study authors say the majority of global food supply relies on a relatively small number of crops, and greater diversity would boost food security by improving climate resilience.

    There would also be potentially positive impacts on wider biodiversity and people’s diets. Legumes such as chickpeas and soybeans, which have recently had their first commercial UK harvests, are important protein sources. This would support shifts from heavy meat consumption to a more balanced diet and lower carbon footprint. Legumes also add nitrogen to the soil, reducing the reliance on fertilisers.

    Risks and challenges

    While a changing climate across the UK is expected to support a range of new crops, the study shows the largest increases in suitability will be in the southwest and Scottish borders due to the benefits of rising temperatures not being cancelled out by more restricted water in the summer, especially under 4 degrees of warming.

    However, many areas outside the South East and East Anglia have small field sizes, variable topography and are far from the current food processing and supply chains, limiting potential shifts in production to these regions. There are also economic risks investing in new agronomic practices and technology, and potential environmental risks in introducing crops to new locations, including interactions with pollinators, wild crop relatives and pests.

    Dr Redhead said: “Clearly, it’s unlikely to be feasible just to switch large-scale food production from Britain’s agricultural heartlands of southeastern England to Scotland, for example. However, climate change is happening now, and its impacts will increase by 2080, so whatever action is taken will involve big challenges in terms of where our food comes from and the way our agricultural landscapes are managed.”

    Solutions

    Methods to cope with the challenges highlighted in the study include:

    • Further research into the viability of the crops that are identified as ‘winners’.
    • Changing our agricultural supply and distribution networks to better support farmers growing new crops in new locations.
    • Adopting new agricultural systems such as paludiculture (wet farming) and indoor vertical farming.
    • Breeding and growing more heat- or drought-resilient varieties of existing staple crops.

    No-one can definitively say whether a certain crop will be possible or profitable in 2080, but the study addresses the gap in knowledge about climatic suitability of crops in particular regions, which is currently limiting uptake of new produce.

    Study co-author Professor Rachel Warren of the Tyndall Centre for Climate Change Research at UEA said: “Without such data, agricultural systems are likely to be ‘locked in’ to current crops, with adaptations failing to keep pace with climate change or relying on practices that exacerbate its impacts, such as heavy irrigation that would increase water scarcity.

    “Major changes to agricultural systems and diets can take decades to implement and so our long-term projections provide important information well ahead of time for farmers, supermarkets, researchers, policymakers and the public on the opportunities, challenges and trade-offs involved in adapting to the impacts of climate change.”

    The study authors say their horizon-scanning approach on how climate change affects suitability of crops could be transferable to other countries.

  • New tool enables phylogenomic analyses of entire genomes

    New tool enables phylogenomic analyses of entire genomes

    Researchers led by electrical engineers at the University of California San Diego have developed a better way to perform the comparative analysis of entire genomes. This approach can be used to study relationships between different species across geological time scales.

    This new approach is poised to unlock discoveries regarding how evolution has shaped present-day genomes and also how the tree of life is organized. The new method, named CASTER, is described in a paper published in Science on 23 January 2025.

    CASTER is poised to offer biologists a far more scalable approach than state-of-the-art for comparing full genomes. This is especially relevant given the exploding number of sequenced genomes of both living and also extinct species. All these genomes are available for comparative study through phylogenomic analyses. CASTER provides interpretable outputs that will help biologists understand not only the species relationships but also the mosaic of evolutionary histories across the genome.

    “Since the early 2000s, countless studies have claimed ‘genome-wide’ phylogeny reconstruction; however, these have been all based on subsampling regions scattered across the genomes, totalling only a small fraction of each full genome that is part of any given study. Analyzing all genomic positions while using complex models had seemed out of reach,” said Siavash Mirarab, an electrical engineering professor at UC San Diego and the corresponding author of the new paper in Science “What excites me is that we can now perform truly genome-wide analyses using every base pair aligned across species with widely available computational resources.”

    “Arriving at this milestone required combined efforts from many disciplines, including statistics, computer science, and biology,” said Chao Zhang, the first author on the new Science paper. Zhang completed his PhD at UC San Diego in the Bioinformatics and Systems Biology program. He is now on the faculty of the University of Copenhagen.

  • Final synthetic yeast chromosome unlocks new era in biotechnology

    Final synthetic yeast chromosome unlocks new era in biotechnology

    Macquarie University researchers have worked with an international team of scientists to achieve a major milestone in synthetic biology by completing the creation of the final chromosome in the world’s first synthetic yeast genome.

    This achievement represents the completion of the global Sc2.0 project to create the world’s first synthetic eukaryotic genome from Saccharomyces cerevisiae (baker’s yeast) and a new-to-nature tRNA neochromosome.

    Using cutting-edge genome-editing techniques, including the CRISPR D-BUGS protocol, the team identified and corrected genetic errors that impacted yeast growth. These changes restored the strain’s ability to grow on glycerol, a key carbon source, under elevated temperatures.

    The breakthrough, published this week in Nature Communications, demonstrates how engineered chromosomes can be designed, built and debugged to create more resilient organisms that could help secure supply chains for food and medicine production in the face of climate change and future pandemics.

    “This is a landmark moment in synthetic biology,” says Professor Sakkie Pretorius, Co-Chief Investigator and Deputy Vice Chancellor (Research) of Macquarie University.

    “It is the final piece of a puzzle that has occupied synthetic biology researchers for many years now.”

    Distinguished Professor Ian Paulsen, Director of the ARC Centre of Excellence in Synthetic Biology, who co-led the project, says: “By successfully constructing and debugging the final synthetic chromosome, we’ve helped complete a powerful platform for engineering biology that could revolutionise how we produce medicines, sustainable materials and other vital resources.”

    The research team used specialised gene editing tools to identify and fix problems in the synthetic chromosome affecting how well the yeast could reproduce and grow under challenging conditions.

    They discovered that the placement of genetic markers near uncertain gene regions accidentally interfered with how essential genes were turned on and off, particularly affecting crucial processes like copper metabolism and how cells divide their genetic material.

    “One of our key findings was how the positioning of genetic markers could disrupt the expression of essential genes,” says co-lead author Dr Hugh Goold, research scientist at The NSW Department of Primary Industries and Honorary Postdoctoral Research Fellow from Macquarie University’s School of Natural Sciences.

    “This discovery has important implications for future genome engineering projects, helping establish design principles that can be applied to other organisms.”

    The completion of the chromosome known as synXVI allows scientists to explore new possibilities in metabolic engineering and strain optimisation. The synthetic chromosome includes features that enable researchers to generate genetic diversity on demand, accelerating the development of yeasts with enhanced capabilities for biotechnology applications.

    “The synthetic yeast genome represents a quantum leap in our ability to engineer biology,” says Dr Briardo Llorente, Chief Scientific Officer at the Australian Genome Foundry.

    The construction of such a large synthetic chromosome was only possible using the robotic instrumentation in the Australian Genome Foundry.

    “This achievement opens up exciting possibilities for developing more efficient and sustainable biomanufacturing processes, from producing pharmaceuticals to creating new materials,” says Dr Llorente.

    The research provides valuable insights for future synthetic biology projects, including potential applications in engineering plant and mammalian genomes. The team’s new design principles for synthetic chromosomes to avoid placing potentially disruptive genetic elements near important genes will help other researchers working on synthetic chromosomes.

    Macquarie University is responsible for more than 12 per cent of the entire Sc 2.0 project, and this contribution was supported by the NSW Government’s Department of Primary Industries, the Australian Research Council Centre of Excellence in Synthetic Biology, and external grants from Bioplatforms Australia and the NSW Chief Scientist and Engineer.