The viscoelastic nature of biological cells has emerged as an increasingly important research subject due to its relevance for cellular functions under physiological and pathological conditions. Advancements in microfluidics have made this technology a promising tool to study the viscoelasticity of cells. However, significant challenges remain, including the complex distribution of stresses acting on cells depending on the channel geometry, and the difficulty of keeping cells in the focal plane for imaging. Here, we report a new approach using hyperbolic channels for measuring cell viscoelasticity. A channel height much larger than the typical cell size minimized shear stresses so that normal stresses in the hyperbolic region dominated the stress distribution. Reducing the complexity of the stress-strain relationship allowed us to use polyacrylamide microgel beads to calibrate the stress curve. Additionally, we introduced 3D hydrodynamic focusing which enabled us to focus cells and microgel beads in the center of the channel. Finally, Kelvin-Voigt and power-law rheology models were employed to extract the mechanical properties of microgel beads and human leukemia HL60 cells. The measurement technique described here will help establish the viscoelastic properties of cells as an important readout in biophysical research in health and disease.
Quantitative phase deformability cytometry (QP-DC) for precise and clinically relevant multiparametric immune cell profiling
Kyoohyun Kim,
Eoghan O’Connell,
Christine Schauer,
Janina Schoen,
Jiwoo Shim,
Florian Mayerle,
Philipp Radler,
Philipp Lebhardt,
Martin Kräter, et al.
bioRxiv 2025.10.27.684875v1
(2025)
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Imaging flow cytometry enables the detailed analysis of cell morphology and internal structures through high-throughput cell imaging, and quantitative phase imaging (QPI)-based microfluidic approaches have extended this by providing label-free measures such as dry mass and refractive index (RI). Building on these developments, we present quantitative phase deformability cytometry (QP-DC), which integrates QPI with deformability cytometry to simultaneously measure morphology, mechanics, and intrinsic biophysical parameters such as mass density and dry mass. Numerical refocusing ensures in-focus images independent of axial position, improving precision in contour detection and feature extraction. Using microspheres and whole blood, we validated QP-DC and then applied it to neutrophils under lipopolysaccharide (LPS) stimulation and from patients with systemic lupus erythematosus (SLE). QP-DC revealed LPS-induced reductions in neutrophil mass density and identified heterogeneous subpopulations in SLE. These results demonstrate the capability of QP-DC for precise biophysical and mechanical characterization, offering significant potential for research and clinical diagnostics.
Biphasic inflammation control by fibroblasts enables spinal cord regeneration in zebrafish
Nora John,
Thomas Fleming,
Julia Kolb,
Olga Lyraki,
Sebastián Vásquez-Sepúlveda,
Asha Parmer,
Kyoohyun Kim,
Maria Tarczewska,
Kanwarpal Singh, et al.
Fibrosis and persistent inflammation are interconnected processes that inhibit axon regeneration in the mammalian central nervous system (CNS). In zebrafish, by contrast, fibroblast-derived extracellular matrix deposition and inflammation are tightly regulated to facilitate regeneration. However, the regulatory cross-talk between fibroblasts and the innate immune system in the regenerating CNS remains poorly understood. Here, we show that zebrafish fibroblasts possess a dual role in inducing and resolving inflammation, which are both essential for regeneration. We identify a transient, injury-specific cthrc1a+ fibroblast state with an inflammation-associated, less differentiated, and non-fibrotic profile. Induction of this fibroblast state precedes and contributes to the initiation of the inflammatory response. At the peak of neutrophil influx, cthrc1a+ fibroblasts coordinate the resolution of inflammation. Disruption of these inflammation dynamics alters the mechano-structural properties of the lesion environment and inhibits axon regeneration. This establishes the biphasic inflammation control by dedifferentiated fibroblasts as a pivotal mechanism for CNS regeneration.
Optical Measurement of the Mass Densitiy of Biological Samples
Mass density is a vital property for the improved biophysical understanding of and within biological samples. It is increasingly attracting active investigations, but still lacks reliable, noncontact techniques to accurately characterize it in biological systems. Contrary to popular belief, refractive index information alone is insufficient to determine a sample’s mass density, as we demonstrate here theoretically and experimentally. Instead, we measured the nonlinear gain of stimulated Brillouin scattering to provide additional information for the mass density estimation. This all-optical method reduces the estimation error 10-fold, offering a more accurate and universal technique for mass density measurements.
Automation and improvement of WBC mechanical profiling in deformability cytometry
Sara Kaliman,
Shada Abuhattum Hofemeier,
Benedikt Hartmann,
Jochen Guck
Deformability cytometry (DC) is a powerful biophysical technique that enables cost-effective, high-throughput characterization of disease-associated changes in blood cell mechanics. Mechanical profiling of living white blood cells (WBCs) is particularly valuable due to their critical role in the immune response. However, reliably identifying and classifying WBC subtypes in a label-free manner remains a significant challenge. Until now, the analysis pipeline has relied on manual gating by trained experts, limiting scalability and reproducibility. In this study, we present a fully automated and generalizable framework for WBC classification in shear flow DC experiments, based on box filters and unsupervised clustering of cell populations. Both box filters and unsupervised clustering rely on cell shape features derived from high-accuracy segmentation and on cell texture features derived from bright-field images. This unsupervised approach not only improves reproducibility and reduces processing time but also overcomes key limitations of supervised models that require extensive training data and often suffer from reduced performance under varying imaging conditions. We validated our method by comparing cell features obtained through manual gating and automated classification across six experimental sets. These sets incorporated variations in blood donors, anticoagulants (EDTA and citrate), blood collection sources (capillary and venous), and device brightness settings. Each set included five repeated measurements. The results consistently confirmed the reliability and robustness of the method across all tested conditions and WBC types. Importantly, this automated pipeline enables the inclusion of WBCs with membrane protrusions—typically excluded from standard analyses—allowing for morphological characterization of potentially activated cells. Moreover, by using shape features derived from the original contour rather than the convex hull, we improve morphological accuracy and reduce measurement variability. This approach thus enhances the accuracy, consistency, and scalability of WBC mechanophenotyping and enables high-throughput analysis across large cohorts.
Although micron-sized microgels have become important building blocks in regenerative materials, offering decisive interactions with living matter, their chemical composition mostly significantly varies when their network morphology is tuned. Since cell behavior is simultaneously affected by the physical, chemical, and structural properties of the gel network, microgels with variable morphology but chemical equivalence are of interest. This work describes a new method to produce thermoresponsive microgels with defined mechanical properties, surface morphologies, and volume phase transition temperatures. A wide variety of microgels is synthesized by crosslinking monomers or star polymers at different temperatures using thermally assisted microfluidics. The diversification of microgels with different network structures and morphologies but of chemical equivalence offers a new platform of microgel building blocks with the ability to undergo phase transition at physiological temperatures. The method holds high potential to create soft and dynamic materials while maintaining the chemical composition for a wide variety of applications in biomedicine.
A label-free method for measuring the composition of multicomponent biomolecular condensates
Patrick M. McCall,
Kyoohyun Kim,
Anna Shevchenko,
Martine Ruer-Gruß,
Jan Peychl,
Jochen Guck,
Andrej Shevchenko,
Anthony A. Hyman,
Jan Brugués
Many subcellular compartments are biomolecular condensates made of multiple components, often including several distinct proteins and nucleic acids. However, current tools to measure condensate composition are limited and cannot capture this complexity quantitatively because they either require fluorescent labels, which can perturb composition, or can distinguish only one or two components. Here we describe a label-free method based on quantitative phase imaging and analysis of tie-lines and refractive index to measure the composition of reconstituted condensates with multiple components. We first validate the method empirically in binary mixtures, revealing sequence-encoded density variation and complex ageing dynamics for condensates composed of full-length proteins. We then use analysis of tie-lines and refractive index to simultaneously resolve the concentrations of five macromolecular solutes in multicomponent condensates containing RNA and constructs of multiple RNA-binding proteins. Our measurements reveal an unexpected decoupling of density and composition, highlighting the need to determine molecular stoichiometry in multicomponent condensates. We foresee this approach enabling the study of compositional regulation of condensate properties and function.
Direct, high-throughput linking of single cell imaging and gene expression
Catherine Xu,
Georg Meisl,
Nikita Moshkov,
Karolis Goda,
Alexey Shkarin,
Maximilian Schlögel,
Tuomas PJ Knowles,
Linas Mazutis,
Jochen Guck
bioRxiv 10.1101/2025.09.01.672798
(2025)
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Single-cell technologies have transformed our ability to dissect cellular heterogeneity by enabling measurements of individual molecular modalities, from genome and transcriptome to proteome and metabolome. However, at the single-cell level, the physical properties of cells, such as size, morphology, and mechanical state, remain largely disconnected from molecular profiling, limiting our understanding of the relationship between these aspects of cellular phenotype and gene expression. We introduce im-seq, a high-throughput, flow-based platform that integrates live-cell imaging with droplet-based mRNA sequencing at the single-cell level. By optically barcoding individual cells, im-seq enables the joint capture of physical and transcriptional profiles from single cells. We demonstrate that this multimodal approach can resolve physical and molecular features across cell lines, to reveal genes associated with phenotypic properties at unprecedented resolution. Our results establish im-seq as a versatile high-throughput framework for linking genetic information to physical properties, providing the large scale, information-dense datasets needed to power the next generation of data-driven discoveries in cell biology.
Measuring Molecular Mass Densities at Subcellular Resolution Using Optical Diffraction Tomography
Kyoohyun Kim,
Abin Biswas,
Jochen Guck,
Simone Reber
The Nuclear Membrane. Methods in Molecular Biology
119-141
(2025)
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Biological systems intricately regulate their density and volume throughout their life cycles and in response to physiological changes. Mass density, as a fundamental physical quantity, plays significant roles in biological processes such as differentiation, cell growth, protein synthesis, and condensate formation. Loss of density homeostasis on the other hand can have severe consequences including cellular senescence and disease states. Recent developments in biophotonics now enable high-resolution density quantification, providing new insights into the biophysical properties of cells and subcellular structures. One such technique is optical diffraction tomography (ODT), which offers label-free, high-resolution measurements of mass density distribution based on refractive index (RI) measurements. In this chapter, we present a comprehensive guide to implementing ODT for quantitative characterization of mass density distribution in biological systems, including in vivo (adherent cell culture) and in vitro (Xenopus egg extract) samples. We begin by detailing the optical setups, emphasizing key considerations for optimizing tomography acquisition. Subsequently, we introduce preparation protocols tailored to biological samples in various types of sample carriers and offer guidance on standard image acquisition and data analysis procedures. Finally, we address the challenges posed by the linear relationship between RI and mass density in complex substances, offering strategies for overcoming these limitations.
Conserved nucleocytoplasmic density homeostasis drives cellular organization across euraryotes
Abin Biswas,
Omar Muñoz,
Kyoohyun Kim,
Carsten Hoege,
Benjamin M. Lorton,
Rainer Nikolay,
Matthew L. Kraushar,
David Shechter,
Jochen Guck, et al.
Nature Communications
16
7597
(2025)
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The confinement of macromolecules has profound implications for cellular biochemistry. It generates environments with specific physical properties affecting diffusion, macromolecular crowding, and reaction rates. Yet, it remains unknown how intracellular density distributions emerge and affect cellular physiology. Here, we show that the nucleus is less dense than the cytoplasm and that living systems establish a conserved density ratio between these compartments due to a pressure balance across the nuclear envelope. Nuclear transport establishes a specific nuclear proteome that exerts a colloid osmotic pressure, which, assisted by chromatin pressure, increases nuclear volume. During C. elegans development, the nuclear-to-cytoplasmic density ratio is robustly maintained even when nuclear-to-cytoplasmic volume ratios change. We show that loss of density homeostasis correlates with altered cell functions like senescence and propose density distributions as key markers in pathophysiology. In summary, this study reveals a homeostatic coupling of macromolecular densities that drives cellular organization and function.
Fine-tuning cell-mimicking polyacrylamide microgels: Sensitivity to
microscale reaction conditions in droplet microfluidics
Ruchi Goswami,
Kyoohyun Kim,
Aldo R. Boccaccini,
Jochen Guck,
Salvatore Girardo
The ability to shape polyacrylamide (PAAm) hydrogels using droplet microfluidics enables the production of microgel particles that mimic the cellular physical properties, opening new avenues in mechanobiology. Precisely controlling microgel size and elasticity is crucial yet complex, as various factors influence polymerization and the resulting network structure. While it is well established that chemical reactions in microdroplets are typically faster and more homogeneous than in bulk systems, an often-overlooked aspect is the increased sensitivity of these microreactors: minor variations in chemical or physical parameters can lead to significant changes in the resulting microgel properties. Our study identifies flow conditions as a key factor influencing both microgel elasticity and size, by modulating interfacial transport during gelation. Using a flow-focusing microfluidic chip, we generated pre-gel droplets with identical composition dispersed in an oil phase, systematically varying the PAAm-to-oil flow rate ratio while keeping the total flow rate constant. This approach yields droplets with minimal diameter variation (< 1um), yet produces beads with distinct Young's moduli, despite identical total monomer concentrations. Further analysis revealed that the catalyst transport across the oil-water interface significantly affects the polymerization efficiency and polymer network. Our findings highlight that despite the advantages of droplet-based polymerization, achieving reproducible microgel properties requires careful control of flow parameters. This underscores the importance of precise microfluidic control in advancing PAAm microgel applications in biophysics.
Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy
Kit Gallagher,
Rachel S. Sousa,
Chandler Gatenbee,
Ryan Schenck,
Peng Chen,
Timon Citak,
Sydney Leither,
Lucia Mazzacurati,
Agata Xella, et al.
bioRxiv 10.1101/2025.07.10.664235
(2025)
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High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative “extinction therapy” strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular “magic bullet” but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An “IMOme” score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.
Consensus statement on Brillouin light scattering microscopy of biological materials
Pierre Bouvet,
Carlo Bevilacqua,
Yogeshwari Ambekar,
Giuseppe Antonacci,
Joshua Au,
Silvia Caponi,
Sophie Chagnon-Lessard,
Juergen Czarske,
Thomas Dehoux, et al.
Nature Photonics
19
681-691
(2025)
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Brillouin light scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the submicrometre scale. Over the past decade, BLS has found increasing microscopy applications in the life sciences, driven by the observed importance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and the extension of BLS to an imaging modality. As with other spectroscopic techniques, BLS measurements detect not only signals that are characteristic of the investigated sample, but also those of the experimental apparatus, and can be substantially affected by measurement conditions. Here we report a consensus between researchers in the field. We aim to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artefacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different, often self-built, spectrometers, a consensus statement is particularly timely to ensure unified advancement.
Reinforcement Failing guides the discovery of emergent spatial dynamics in adaptive tumor therapy
Serhii Aif,
Maximilian Eiche,
Nico Appold,
Elias Fischer,
Timon Citak,
Jona Kayser
bioRxiv 10.1101/2025.04.08.647768
(2025)
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Artificial intelligence is revolutionizing oncology by transforming how malignancies are diagnosed, cancer biology is understood, and therapeutics are discovered. A cornerstone of this progress has been the availability of extensive, carefully curated datasets. Similarly, advances in AI-guided therapeutic strategies via Reinforcement Learning (RL) hinge upon carefully designed computational training environments that are both efficient and sufficiently realistic to capture key dynamics of cancer growth and therapy response. However, designing suitable models remains challenging for solid tumors, where emergent physical phenomena significantly influence therapeutic outcomes. Here, we introduce Reinforcement Failing, an AI-guided scientific discovery framework designed to reveal emergent physical mechanisms in tumor therapy. Applying this approach to adaptive therapy in solid tumors, we identify the pivotal roles of position-dependent proliferation and mechanically driven collective motion of resistant cells. Our findings highlight how integrating tumor physics into therapeutic strategies can optimize outcomes while mitigating hidden pitfalls during translation. Together, these results demonstrate that Reinforcement Failing is a powerful artificial scientific discovery engine for deciphering high-complexity processes in personalized cancer treatment and beyond.
Small U-Net for Fast and Reliable Segmentation in Imaging Flow Cytometry
Sara Kaliman,
Raghava Alajangi,
Nadia Sbaa,
Paul Müller,
Nadine Ströhlein,
Jeffrey Harmon,
Martin Kräter,
Jochen Guck,
Shada Abuhattum Hofemeier
Cytometry Part A
107
(7)
450-463
(2025)
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Imaging flow cytometry requires rapid and accurate segmentation methods to ensure high-quality cellular morphology analysis and cell counting. In deformability cytometry (DC), a specific type of imaging flow cytometry, accurately detecting cell contours is critical for evaluating mechanical properties that serve as disease markers. Traditional thresholding methods, commonly used for their speed in high-throughput applications, often struggle with low-contrast images, leading to inaccuracies in detecting the object contour. Conversely, standard neural network approaches like U-Net, though effective in medical imaging, are less suitable for high-speed imaging applications due to long inference times. To address these issues, we present a solution that enables both fast and accurate segmentation, designed for imaging flow cytometry. Our method employs a small U-Net model trained on high-quality, curated, and annotated data. This optimized model outperforms traditional thresholding methods and other neural networks, delivering a 35× speed improvement on CPU over the standard U-Net. The enhanced performance is demonstrated by a significant reduction in systematic measurement errors in blood samples analyzed using DC. The tools developed in this study are adaptable for various imaging flow cytometry applications. This approach improves segmentation quality while maintaining the rapid processing necessary for high-throughput environments.
Cell state-specific cytoplasmic density controls spindle architecture and scaling
Nature Cell Biology
27
959-971
(2025)
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Mitotic spindles are dynamically intertwined with the cytoplasm they assemble in. How the physicochemical properties of the cytoplasm affect spindle architecture and size remains largely unknown. Using quantitative biochemistry in combination with adaptive feedback microscopy, we investigated mitotic cell and spindle morphology during neural differentiation of embryonic stem cells. While tubulin biochemistry and microtubule dynamics remained unchanged, spindles changed their scaling behaviour; in differentiating cells, spindles were considerably smaller than those in equally sized undifferentiated stem cells. Integrating quantitative phase imaging, biophysical perturbations and theory, we found that as cells differentiated, their cytoplasm became more dilute. The concomitant decrease in free tubulin activated CPAP (centrosomal P4.1-associated protein) to enhance the centrosomal nucleation capacity. As a consequence, in differentiating cells, microtubule mass shifted towards spindle poles at the expense of the spindle bulk, explaining the differentiation-associated switch in spindle architecture. This study shows that cell state-specific cytoplasmic density tunes mitotic spindle architecture. Thus, we reveal physical properties of the cytoplasm as a major determinant in organelle size control.
Hierarchical Heterogeneities in Spatio-Temporal Dynamics of the Cytoplasm
Understanding of the dynamics inherent to biological matter is crucial for illuminating the physical mechanisms underlying cellular processes. In this study, we employ bright-field differential dynamic microscopy (DDM) to investigate density fluctuations inherent in a cell-free model of eukaryotic cytoplasm. Our measurements reveal subdiffusive fractional Brownian motion and non-Gaussian displacement distributions, highlighting cytoplasmic heterogeneity. We introduce an empirical model that combines fractional Brownian motion with an inverse Gaussian distribution of diffusivities to describe the observed non-Gaussianity. Validated through Monte Carlo simulations, this model allows us to estimate the fractional diffusivity and exponent effectively. By altering macromolecular composition, the addition of energy, and assembly of a cytoskeleton, we identify three independent mechanisms that result in similar fractional exponents yet distinct diffusivities. We find that energy addition leads to non-stationary dynamics, in contrast to the stationary behavior observed under passive conditions. Presence of microtubules introduces a secondary dynamical timescale, which we describe using a two-state fractional Brownian motion model to differentiate between cytosolic and microtubule network associated contributions. Our findings demonstrate the effectiveness of DDM as a label-free tool for quantifying viscoelastic and heterogeneous properties of the cytoplasm and provide insights into how physical and biochemical factors, including cytoskeletal organization, govern subcellular dynamics.
Selective plane illumination microscopy (SPIM), also known as light sheet fluorescence microscopy, provides high specificity through fluorescence labeling. However, it lacks complementary structural information from the surrounding context, which is essential for the comprehensive analysis of biological samples. Here, we present a high-resolution, multimodal imaging system that integrates SPIM with full-field optical coherence tomography (FF-OCT), without requiring modifications to the existing SPIM setup. Both SPIM and FF-OCT offer low phototoxicity and intrinsic optical sectioning, making them well-suited for in vivo imaging. Their shared detection path enables seamless and efficient co-registration of fluorescence and structural data. We demonstrate the functionality of this combined system by performing in vivo imaging of zebrafish larvae.
Femtosecond Fieldoscopy for super-resolution label-free microscopy
Soyeon Jun,
Andreas Herbst,
Kilian Scheffter,
Daniel Wehner,
Anchit Srivastava,
Hanieh Fattahi
Accessing complete electric field information of a laser pulse interacting with a medium at visible to near-infrared (near-petahertz) frequencies has traditionally required complex laboratory systems operating in vacuum conditions. Recent advancements, however, have enabled the measurement of electric fields at near-petahertz frequencies in ambient air. This capability is critical for understanding ultrafast phenomena and for achieving quantitative detection of molecular species in various samples. This article introduces Femtosecond Fieldoscopy, a field-resolved detection technique for label-free spectroscopy and microscopy. This approach delivers exceptional detection sensitivity and dynamic range at petahertz bandwidths by combining attosecond temporal resolution with temporal isolation of target molecular responses from environmental and excitation pulse effects. Furthermore, Femtosecond Fieldoscopy holds promise for achieving sub-diffraction spatial resolution, opening new horizons for high-precision label-free spectro-microscopy.
De novo identification of universal cell mechanics gene signatures
Marta Urbanska,
Yan Ge,
Maria Winzi,
Shada Abuhattum Hofemeier,
Syed Shafat Ali,
Maik Herbig,
Martin Kräter,
Nicole Toepfner,
Joanne Durgan, et al.
Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.
Thermally Assisted Microfluidics to Produce Chemically Equivalent Microgels with Tunable Network Morphologies
Dirk Rommel,
Bernhard Häßel,
Philip Pietryszek,
Matthias Mork,
Oliver Jung,
Meike Emondts,
Nikita Norkin,
Iris Christine Doolaar,
Yonka Kittel, et al.
Angewandte Chemie, International Edition in English
64
(1)
(2025)
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Although micron-sized microgels have become important building blocks in regenerative materials, offering decisive interactions with living matter, their chemical composition mostly significantly varies when their network morphology is tuned. Since cell behavior is simultaneously affected by the physical, chemical, and structural properties of the gel network, microgels with variable morphology but chemical equivalence are of interest. This work describes a new method to produce thermoresponsive microgels with defined mechanical properties, surface morphologies, and volume phase transition temperatures. A wide variety of microgels is synthesized by crosslinking monomers or star polymers at different temperatures using thermally assisted microfluidics. The diversification of microgels with different network structures and morphologies but of chemical equivalence offers a new platform of microgel building blocks with the ability to undergo phase transition at physiological temperatures. The method holds high potential to create soft and dynamic materials while maintaining the chemical composition for a wide variety of applications in biomedicine.
Cytoskeleton-functionalized synthetic cells with life-like mechanical features and regulated membrane dynamicity
Sebastian Novosedlik,
Felix Reichel,
Thijs van Veldenhuisen,
Yudong Li,
Hanglong Wu,
Henk Janssen,
Jochen Guck,
Jan van Hest
Nature Chemistry
17
356-364
(2025)
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The cytoskeleton is a crucial determinant of mammalian cell structure and function, providing mechanical resilience, supporting the cell membrane and orchestrating essential processes such as cell division and motility. Because of its fundamental role in living cells, developing a reconstituted or artificial cytoskeleton is of major interest. Here we present an approach to construct an artificial cytoskeleton that imparts mechanical support and regulates membrane dynamics. Our system involves amylose-based coacervates stabilized by a terpolymer membrane, with a cytoskeleton formed from polydiacetylene fibrils. The fibrils bundle due to interactions with the positively charged amylose derivative, forming micrometre-sized structures mimicking a cytoskeleton. Given the intricate interplay between cellular structure and function, the design and integration of this artificial cytoskeleton represent a crucial advancement, paving the way for the development of artificial cell platforms exhibiting enhanced life-like behaviour.
Contact
Cell Physics Division Prof. Vahid Sandoghdar Acting Division Head
Max Planck Institute for the Science of Light Staudtstr. 2 91058 Erlangen, Germany
