Immuno-Materialien

Microfluidics plays a pivotal role in organ-on-chip technologies and in the study of synthetic cells, especially in the development and analysis of artificial cell models. However, approaches that use synthetic cells as integral functional components for microfluidic systems to shape the microenvironment of natural living cells cultured on-chip are not explored. Here, colloidosome-based synthetic cells are integrated into 3D microfluidic devices, pioneering the concept of synthetic cell-based microenvironments for organs-on-chip. Methods are devised to create dense and stable networks of silica colloidosomes, enveloped by supported lipid bilayers, within microfluidic channels. These networks promote receptor-ligand interactions with on-chip cultured cells. Furthermore, a technique is introduced for the controlled release of growth factors from the synthetic cells into the channels, using a calcium alginate-based hydrogel formation within the colloidosomes. To demonstrate the potential of the technology, a modular plug-and-play lymph-node-on-a-chip prototype that guides the expansion of primary human T cells by stimulating receptor ligands on the T cells and modulating their cytokine environment is presented. This integration of synthetic cells into microfluidic systems offers a new direction for organ-on-chip technologies and suggests further avenues for exploration in potential therapeutic applications.

Abstract Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its ‘Minimal Information for Studies of Extracellular Vesicles’, which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

The expansion of T cells ex vivo is crucial for effective immunotherapy but currently limited by a lack of expansion approaches that closely mimic in vivo T cell activation. Taking inspiration from bottom-up synthetic biology, a new synthetic cell technology is introduced based on dispersed liquid-liquid phase-separated droplet-supported lipid bilayers (dsLBs) with tunable biochemical and biophysical characteristics, as artificial antigen presenting cells (aAPCs) for ex vivo T cell expansion. These findings obtained with the dsLB technology reveal three key insights: first, introducing laterally mobile stimulatory ligands on soft aAPCs promotes expansion of IL-4/IL-10 secreting regulatory CD8+ T cells, with a PD-1 negative phenotype, less prone to immune suppression. Second, it is demonstrated that lateral ligand mobility can mask differential T cell activation observed on substrates of varying stiffness. Third, dsLBs are applied to reveal a mechanosensitive component in bispecific Her2/CD3 T cell engager-mediated T cell activation. Based on these three insights, lateral ligand mobility, alongside receptor- and mechanosignaling, is proposed to be considered as a third crucial dimension for the design of ex vivo T cell expansion technologies.

Synthetic cells can advance immunotherapy, offering innovative approaches to understanding and enhancing immune responses. This review article delves into the advancements and potential of synthetic cell technologies in immunology, emphasizing their role in understanding and manipulating immune functions. Recent progress in understanding vertebrate immune systems and the challenges posed by diseases highlight the need for innovative research methods, complementing the analysis of multidimensional datasets and genetic engineering. Synthetic immune cell engineering aims to simplify the complexity of immunological systems by reconstructing them in a controlled setting. This approach, alongside high-throughput strategies, facilitates systematic investigations into immunity and the development of novel treatments. The article reviews synthetic cell technologies, focusing on their alignment with the three laws of immunity: universality, tolerance, and appropriateness. It explores the integration of synthetic cell modules to mimic processes such as controlled T-cell activation, bacteria engulfment and elimination, or cellular maturation into desirable phenotypes. Together, such advancements expand the toolbox for understanding and manipulating immune functions. Synthetic cell technologies stand at the innovation crossroads in immunology, promising to illuminate fundamental immune system principles and open new avenues for research and therapy.

Background: Due to COVID-19, pandemic preparedness emerges as a key imperative, necessitating new approaches to accelerate development of reagents against infectious pathogens. Methods: Here, we developed an integrated approach combining synthetic, computational and structural methods with in vitro antibody selection and in vivo immunization to design, produce and validate nature-inspired nanoparticle-based reagents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Results: Our approach resulted in two innovations: (i) a thermostable nasal vaccine called ADDoCoV, displaying multiple copies of a SARS-CoV-2 receptor binding motif derived epitope and (ii) a multivalent nanoparticle superbinder, called Gigabody, against SARS-CoV-2 including immune-evasive variants of concern (VOCs). In vitro generated neutralizing nanobodies and electron cryo-microscopy established authenticity and accessibility of epitopes displayed by ADDoCoV. Gigabody comprising multimerized nanobodies prevented SARS-CoV-2 virion attachment with picomolar EC50. Vaccinating mice resulted in antibodies cross-reacting with VOCs including Delta and Omicron.
Conclusion: Our study elucidates Adenovirus-derived dodecamer (ADDomer)-based nanoparticles for use in active and passive immunization and provides a blueprint for crafting reagents to combat respiratory viral infections.

An unresolved issue in contemporary biomedicine is the overwhelming
number and diversity of complex images that require annotation, analysis and
interpretation. Recent advances in Deep Learning have revolutionized the field
of computer vision, creating algorithms that compete with human experts in
image segmentation tasks. However, these frameworks require large human-
annotated datasets for training and the resulting “black box” models are dif-
ficult to interpret. In this study, we introduce Kartezio, a modular Cartesian
Genetic Programming-based computational strategy that generates fully
transparent and easily interpretable image processing pipelines by iteratively
assembling and parameterizing computer vision functions. The pipelines thus
generated exhibit comparable precision to state-of-the-art Deep Learning
approaches on instance segmentation tasks, while requiring drastically smaller
training datasets. This Few-Shot Learning method confers tremendous flex-
ibility, speed, and functionality to this approach. We then deploy Kartezio to
solve a series of semantic and instance segmentation problems, and demon-
strate its utility across diverse images ranging from multiplexed tissue histo-
pathology images to high resolution microscopy images. While the flexibility,
robustness and practical utility of Kartezio make this fully explicable evolu-
tionary designer a potential game-changer in the field of biomedical image
processing, Kartezio remains complementary and potentially auxiliary to
mainstream Deep Learning approaches.