Biology subjects

Dysthe, D. K.

Publications and source records attributed to Dysthe, D. K..

5 recordsLinked to original sources

bioRxiv2026

A cellular automaton model of osteogenic differentiation reveals identifiability limits of endpoint assays

Standard differentiation assays sample cell states only at discrete time points, while the underlying progression unfolds continuously and heterogeneously across cells. As a result, different combinations of proliferation, commitment, and maturation dynamics can converge to similar endpoint measurements. This many-to-one mapping between latent trajectories and observable readouts constitutes a partially observed inverse problem that limits mechanistic interpretation. Although this ambiguity is inherent to many experimental systems, it is rarely examined using models that connect cell-state dynamics to assay-level quantities. We present OsteoMin, a coarse-grained cellular automaton that links stochastic transitions between pre-osteoblast and osteoblast states to experimentally measurable readouts of alkaline phosphatase activity, collagen deposition, and mineralization. Model parameters were constrained using literature-reported kinetics and evaluated against dexamethasone and menaquinone-4 perturbations. The frame-work reproduces qualitative assay trends and enables systematic analysis of how cell-state progression, matrix maturation, and external perturbations shape differentiation outcomes. Using this framework, we quantify the identifiability limits of endpoint assays and test whether standard measurements can distinguish underlying differentiation mechanisms. Distinct perturbation families often produce similar endpoint responses (macro-F1 {approx} 0.42), indicating limited discriminative power. Incorporating temporal trajectories improves separability (macro-F1 {approx} 0.78), demonstrating that most identifiable information resides in marker dynamics rather than terminal measurements. Sobol analysis shows early markers depend on proliferation timing, whereas late mineralization is governed by nonlinear matrix maturation and parameter interactions. Together, these results show that endpoint assays constrain overall progression but do not uniquely identify underlying mechanisms. OsteoMin provides a framework linking differentiation dynamics to assay observables and a basis for assessing identifiability in endpoint-driven systems.

bioengineering↗

bioRxiv2026

Three Dimensional Dynamics of Epithelial Monolayers

Cells in epithelial monolayers are commonly quantified using a projected area and implicitly assuming constant cell volume and prism-shaped cells. These 2.5D assumptions ignore the volume and mass dynamics that may accompany density fluctuation in confluent space-filling tissues but remain inaccessible to the labelling- and intensity-based imaging techniques used to date. Here, we use time-lapse quantitative phase imaging (QPI) to obtain spatially and temporally resolved maps of height, volume, and dry mass in MDCK epithelial monolayers under physiological conditions. Three findings follow. (i) Cellular dry mass concentration is maintained to within [~] 4.5 % across the monolayer and over time, even during large-amplitude pulsations, ruling out fluid (water) transport as the dominant driver of height and area dynamics. (ii) The mean height of the monolayer increases from [~] 5.5 to [~] 9 {micro}m and the mean cell volume decreases by [~] 25 % as the cell density doubles, evidence of contact inhibition of cell size rather than constant cell volume. Cell heights vary up to 30%, oscillate with a [~] 5 h period, and follow gamma-shaped distributions. (iii) At cellular scales, both segmented-cell tracking and continuum mass-flux analysis show that projected cell volume is not conserved; mass conservation is recovered only after coarse-graining over [~] 2 cell diameters and [~] 1.6 h. Two non-exclusive mechanisms may explain this: non-prismatic cell geometry produces apparent volume fluctuations even at constant true volume, and periodic mass exchange between cells and the ECM produces genuine cyclic changes in cellular dry mass. A propagating elastic mode in the dry-mass structure factor (cs [~] 15 {micro}m/h) further indicates that the monolayer behaves as a viscoelastic active medium rather than a plug-flow fluid. Each of these results requires simultaneous, time-resolved access to height and dry mass at the cellular scale and is therefore newly visible with QPI applied to epithelial monolayers. Together, they question the common 2.5D assumptions of prism-shaped cells, constant volume, plug-flow kinematics, and argue that quantitative continuum and cell-based models of epithelial dynamics must incorporate dry-mass-density regulation and 3D cell geometry.

biophysics↗

bioRxiv2026

Quantitative Phase Imaging of Epithelial Monolayer Dynamics

This study uses two different quantitative phase imaging techniques (QPI) and for the first time measures the height, volume, and mass dynamics of Madin-Darby Canine Kidney (MDCK) monolayers. We demonstrate novel methods to determine the height of confluent monolayers of cells from 2D and 3D QPI data and validate that the two methods agree. We developed a novel cell segmentation method adapted to QPI images of confluent cell layers and present a robust measure of relative error. We also demonstrate that height statistics of cells can be obtained without segmenting the images. We obtain the following precisions of cell density (1 %), height (3 %), area (5 %) and volume (6 %). Cell height varies 15-25 % over a monolayer and increases 50-100 % when cell density doubles. The average refractive index and the dry mass fraction of the cells, on the other hand, are constant over the entire density range.

biophysics↗

bioRxiv2025

Deep Learning for Predicting Stem Cell Efficiency for use in Beta Cell Differentiation

Recent clinical trial data have shown that cell therapy holds curative potential for type-1 diabetes, however the large amounts of lab-grown cells required is a substantial bottleneck. The necessary cell differentiation process exhibits substantial variability, even among clones of induced pluripotent stem cells generated from the same patient. In contrast to many established data sets in medical imaging, human experts struggle to see the difference between highly- and lowly-efficient cell clones. We therefore propose an image-based deep learning model to guide the selection of the most efficient stem cell clones. We apply different deep learning model architectures to learn the morphological differences between good and bad stem cell clones and classify them based on only phase-contrast imaging. To gain critical insight into the learned features and enhance trust in our model, we use layer-wise relevance propagation (LRP), and Fourier-based frequency analysis. Using an EfficientNet-V2-S model, we obtain a novel early prediction for the outcome of the differentiation process from patient-derived stem cell to {beta}-cells using only phase-contrast images. Clone level accuracy is 76.7 % at 24 hours and 96.7 % at 53 hours after start of differentiation. The LRP-generated attribution maps and structure factor analysis show that the structure of the cell population is an important predictive feature. All in all, we present a highly predictive model for successful stem cell differentiation from phase-contrast images, which learns biologically relevant features. This study is a proof-of-concept that deep learning combined with label-free imaging can guide selection of induced pluripotent stem cell clones, thereby reduce cost of {beta}-cell production, and bring curative treatments one step closer to the clinic.

bioinformatics↗

bioRxiv2023

Acoustic wave-induced stroboscopic optical mechanotyping of adherent cells

In this study, we present a novel, high content technique using an innovative cylindrical acoustic transducer, stroboscopic fast imaging and homodyne detection to recover the mechanical properties (dynamic shear modulus) of living adherent cells at low ultrasonic frequencies. By analyzing the micro-oscillations of cells we were able to simultaneously mechanotype whole populations of cells with sub-cellular resolution. The technique can be combined with standard fluorescence imaging allowing to further cross-correlate biological and mechanical information. We demonstrate the potential of the technique by mechanotyping co-cultures of different cell types with significantly different mechanical properties.

biophysics↗