Scientists at The University of Texas at Austin have developed a revolutionary gene-editing method using bacterial retrons that can correct multiple disease-causing mutations at once. Unlike traditional tools limited to one or two mutations, this retron-based system replaces large defective DNA regions, dramatically improving efficiency and inclusivity for patients with complex disorders like cystic fibrosis.
The rise of cell-based therapies, regenerative medicine, and synthetic biology, has created an urgent need for efficient cell engineering, which involves the manipulation of cells for specific purposes. This demand is driven by breakthroughs in cell manufacturing, from fundamental research to clinical therapies. These innovations have come with a deeper understanding of developmental biology, continued optimization of mechanobiological processes and platforms, and the deployment of advanced biotechnological approaches. Induced pluripotent stem cells and immunotherapies like chimeric antigen receptor T cells enable personalized, scalable treatments for regenerative medicine and diseases beyond oncology. But continued development of cell manufacturing and its concomitant clinical advances is hindered by limitations in the production, efficiency, safety, regulation, cost-effectiveness, and scalability of current manufacturing routes. Here, recent developments are examined in cell engineering, with particular emphasis on mechanical aspects, including biomaterial design, the use of mechanical confinement, and the application of micro-and nanotechnologies in the efficient production of enhanced cells. Emerging approaches are described along each of these avenues based on state-of-the-art fundamental mechanobiology. It is called on the field to consider mechanical cues, often overlooked in cell manufacturing, as key tools to augment or, at times, even to replace the use of traditional soluble factors.
Current manufacturing workflows for CAR-based immunotherapies, particularly CAR T, and the emerging CAR NK and CAR macrophage platforms, generally involve four key stages: (i) isolation of primary immune cells or their precursors, (ii) cell activation or differentiation, (iii) genetic modification with CAR constructs, most often via viral vectors or electroporation (EP), and (iv) expansion or preparation for reinfusion. Among these, transfection remains the most critical and technically challenging step, directly influencing the functionality, safety, and scalability of the final product.
In clinical-scale production, EP remains the most widely used non-viral method for gene delivery into immune cells, yet it is increasingly recognized as suboptimal, particularly when delivering large or complex CAR constructs. It suffers from inefficient nuclear delivery, high cell toxicity, and poor functional yields of viable, potent CAR-expressing cells.[ 113 ] These limitations are further exacerbated in more fragile or less permissive cell types, such as NK cells and macrophages, which show lower transfection efficiencies and greater sensitivity to electroporation-induced stress.[ 114 ] Viral vectors, while still dominant in clinical manufacturing, present their own challenges: they are constrained by limited cargo capacity, are costly to produce at scale, and raise regulatory and safety concerns, especially when applied to emerging CAR-NK and CAR macrophage therapies that require flexible, transient, or multiplexed genetic programs.[ 115 ]
In contrast to immune-cell engineering, stem cell-based approaches present a different set of challenges and engineering requirements. While immune cells are genetically modified to enhance cytotoxicity[ 116 ] and specificity or to mitigate excessive T-cell activation,[ 117 ] stem cells must be engineered to control self-renewal, lineage commitment, and functional integration, often requiring precise, non-integrative delivery of genetic or epigenetic modulators (e.g., mRNA, episomal vectors) to maintain cellular identity and safety.[ 118 ] Stem cells hold exceptional therapeutic promise due to their capacity for self-renewal and differentiation into specialized cell types, supporting applications in personalized disease modeling, tissue repair, and organ regeneration.[ 119 ] However, engineering stem cells in a safe, efficient, and clinically relevant manner remains a major challenge. Conventional delivery methods, such as viral vectors and EP, can compromise genomic integrity,[ 120 ] reduce viability,[ 118 ] and induce epigenetic instability,[ 121 ] limiting their translational potential.
What if a soft material could move on its own, guided not by electronics or motors, but by the kind of rudimentary chemical signaling that powers the simplest organisms? Researchers at the University of Pittsburgh Swanson School of Engineering have modeled just that—a synthetic system that on its own directly transforms chemical reactions into mechanical motion, without the need for the complex biochemical machinery present in our bodies.
Just like jellyfish, some of the simplest organisms do not have a centralized brain or nervous system. Instead, they have a “nerve net” which consists of dispersed nerve cells that are interconnected by active junctions, which emit and receive chemical signals. Even without a central “processor,” the chemical signals spontaneously travel through the net and trigger the autonomous motion needed for organisms’ survival.
In a study published in PNAS Nexus, Oleg E. Shklyaev, research assistant, and Anna C. Balazs, Distinguished Professor of Chemical and Petroleum Engineering and the John A. Swanson Chair of Engineering, have developed computer simulations to design a soft material with a “nerve net” that links chemical and mechanical networks in a way that mimics how the earliest and simplest living systems coordinate motion.
A new 3D human brain tissue platform developed by MIT researchers is the first to integrate all major brain cell types, including neurons, glial cells and the vasculature into a single culture. Grown from individual donors’ induced pluripotent stem cells, these models—dubbed Multicellular Integrated Brains (miBrains)—replicate key features and functions of human brain tissue, are readily customizable through gene editing, and can be produced in quantities that support large-scale research.
Although each unit is smaller than a dime, miBrains may be worth a great deal to researchers and drug developers who need more complex living lab models to better understand brain biology and treat diseases.
“The miBrain is the only in vitro system that contains all six major cell types that are present in the human brain,” said Li-Huei Tsai, Picower Professor, director of The Picower Professor of Learning and Memory, and senior author of the study describing miBrains, published in the Proceedings of the National Academy of Sciences.
Networks are systems comprised of two or more connected devices, biological organisms or other components, which typically share information with each other. Understanding how information moves between these connected components, also known as nodes, could help to advance research focusing on numerous topics, ranging from artificial intelligence (AI) to neuroscience.
To measure the directional flow of information in systems, scientists typically rely on a mathematical construct known as transfer entropy, which essentially quantifies the rate at which information is transmitted from one node to another. Yet most strategies for calculating transfer entropy developed so far rely on approximations, which significantly limits their accuracy and reliability.
Researchers at AMOLF, a fundamental physics institute in the Netherlands, recently developed a computational algorithm that can precisely quantify transfer entropy in a wide range of complex networks. Their algorithm, introduced in a paper published in Physical Review Letters, opens new exciting possibilities for the study of information transfer in both biological and engineered networks.
Scientists designed microrobots that use sound to swarm, adapt, and heal themselves — working together like a living organism. The discovery could transform medicine, environmental cleanup, and robotics.
Nature’s Blueprint for Robot Swarms
Animals such as bats, whales, and insects have long relied on sound to communicate and find their way. Drawing inspiration from this, an international group of scientists has developed a model for tiny robots that use sound waves to move and work together in large, coordinated swarms that behave almost intelligently. According to team leader Igor Aronson, Huck Chair Professor of Biomedical Engineering, Chemistry, and Mathematics at Penn State, these robotic collectives could eventually take on challenging missions like exploring disaster areas, cleaning polluted environments, or performing medical procedures inside the human body.
The researchers tested these CAR-NK cells in mice with a human-like immune system. These mice were also injected with lymphoma cells.
Mice that received CAR-NK cells with the new construct maintained the NK cell population for at least three weeks, and the NK cells were able to nearly eliminate cancer in those mice. In mice that received either NK cells with no genetic modifications or NK cells with only the CAR gene, the host immune cells attacked the donor NK cells. In these mice, the NK cells died out within two weeks, and the cancer spread unchecked.
The researchers also found that these engineered CAR-NK cells were much less likely to induce cytokine release syndrome — a common side effect of immunotherapy treatments, which can cause life-threatening complications.
People often compare the genome to a computer’s program, with the cell using its genetic code to process environmental inputs and produce appropriate responses.
But the machine metaphor can be extended even further to any biological system, and applying established concepts of engineering to biology could revolutionize how scientists make their observations within biology, according to research from University of Michigan.
In a paper published in Proceedings of the National Academy of Sciences, Indika Rajapakse, Ph.D., Joshua Pickard, Ph.D. (now an Eric and Wendy Schmidt Postdoctoral Fellow at the Broad Institute), and their team propose that fundamental principles of control theory and observability can be applied to study biological processes that change over time.