Publications Immunophysics Division

Biological functionality of cellular aggregates is largely influenced by the activity and displacements of individual constituent cells. From a theoretical perspective this activity can be characterized by hydrodynamic transport coefficients of diffusivity and conductivity. Motivated by the clustering dynamics of bacterial microcolonies we propose a model of active multicellular aggregates and use recently developed macroscopic fluctuation theory to derive a fluctuating hydrodynamics for this model system. Both semianalytic theory and microscopic simulations show that the hydrodynamic transport coefficients are affected by nonequilibrium microscopic parameters and significantly decrease inside of the clusters. We further find that the Einstein relation connecting the transport coefficients and fluctuations breaks down in the parameter regime where the detailed balance is not satisfied. This study offers valuable tools for experimental investigation of hydrodynamic transport in other systems of cellular aggregates such as tumor spheroids and organoids.

Modern molecular microbiology elucidates the organizational principles of bacterial biofilms via detailed examination of the interplay between signaling and gene regulation. A complementary biophysical approach studies the mesoscopic dependencies at the cellular and multicellular levels with a distinct focus on intercellular forces and mechanical properties of whole biofilms. Here, motivated by recent advances in biofilm research and in other, seemingly unrelated fields of biology and physics, we propose a perspective that links the biofilm, a dynamic multicellular organism, with the physical processes occurring in the extracellular milieu. Using Bacillus subtilis as an illustrative model organism, we specifically demonstrate how such a rationale explains biofilm architecture, differentiation, communication, and stress responses such as desiccation tolerance, metabolism, and physiology across multiple scales—from matrix proteins and polysaccharides to macroscopic wrinkles and water-filled channels.

The quantification of physical properties of biological matter gives rise to novel ways of understanding functional mechanisms. One of the basic biophysical properties is the mass density (MD). It affects the dynamics in sub-cellular compartments and plays a major role in defining the opto-acoustical properties of cells and tissues. As such, the MD can be connected to the refractive index (RI) via the well known Lorentz-Lorenz relation, which takes into account the polarizability of matter. However, computing the MD based on RI measurements poses a challenge, as it requires detailed knowledge of the biochemical composition of the sample. Here we propose a methodology on how to account for assumptions about the biochemical composition of the sample and respective RI measurements. To this aim, we employ the Biot mixing rule of RIs alongside the assumption of volume additivity to find an approximate relation of MD and RI. We use Monte-Carlo simulations and Gaussian propagation of uncertainty to obtain approximate analytical solutions for the respective uncertainties of MD and RI. We validate this approach by applying it to a set of well-characterized complex mixtures given by bovine milk and intralipid emulsion and employ it to estimate the MD of living zebrafish (Danio rerio) larvae trunk tissue. Our results illustrate the importance of implementing this methodology not only for MD estimations but for many other related biophysical problems, such as mechanical measurements using Brillouin microscopy and transient optical coherence elastography.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67–0.99), 0.8 (95% CI: 0.62–0.99), and 0.81 (95% CI: 0.65–0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.

The packing and confinement of macromolecules in the cytoplasm and nucleoplasm has profound implications for cellular biochemistry. How intracellular density distributions vary and affect cellular physiology remains largely unknown. Rather unexpectedly, we had discovered previously that the nucleus has a lower density than the cytoplasm in some cells and that this was robust against various perturbations. Here, we generalize this finding and show that living systems establish and maintain a constant density ratio between the nucleus and the cytoplasm across 10 model organisms: the nucleus is always 20% less dense than the cytoplasm. Using optical diffraction tomography and fluorescence microscopy, various biochemical and cell biological perturbations, together with theoretical modelling, we show that nuclear density is set by a pressure balance across the nuclear envelope in vitro (Xenopus egg extracts), in vivo (cell lines), and during early development (C. elegans embryos). The nuclear proteome exerts a colloid osmotic pressure, which, assisted by entropic chromatin pressure, draws water into the nucleus, while keeping osmotically inactive but heavy and large components excluded. This study reveals a previously unidentified homeostatic coupling of macromolecular densities that drives cellular organization with implications for pathophysiologies such as senescence and cancer.

We develop and implement finite element approximations for the coupled problem of cellular aggregate formation. The equation governing evolution of cell density is convective in nature, necessitating a modification of standard approaches to circumvent the instabilities associated with standard finite element approximations. To this end, a novel mean zero artificial diffusion approach is proposed, in which the classical artificial diffusion term is replaced by one that is orthogonal to its projection on to continuous functions. The resulting approach for the convective equation is shown to be well-posed. A range of numerical results illustrate the stability and accuracy of the new approach, and its behaviour in comparison with an alternative approach using Taylor–Hood elements. The results also provide insights into the behaviour of cellular aggregates in the context of the model studied here.

The international workshop “Interdisciplinary life of microbes: from single cells to multicellular aggregates,” following a virtual preassembly in November 2021, was held in person in Dresden, from 9 to 13 November 2022. It attracted not only prominent experts in biofilm research but also researchers from broadly neighboring disciplines, such as medicine, chemistry, and theoretical and experimental biophysics, both eukaryotic and prokaryotic. Focused brainstorming sessions were the special feature of the event and are at the heart of this commentary.