Speaker
Description
We present a computational framework for the mechanical characterization of leukemic cell nuclei integrating videomicroscopy, image-processing algorithms, and stochastic mechanics. The methodology has been developed within the Leukodomics project, aimed at constructing a digital twin for pediatric acute lymphoblastic leukemia.
Nuclear dynamics are quantified through automated tracking of intranuclear domains. Resulting trajectories are converted into time series of domain positions and analyzed using stochastic process theory. Within a generalized Langevin framework, nuclear fluctuations provide quantitative information on mechanical properties. Tracking data are integrated with confocal fluorescence microscopy, enabling biological annotation with chromatin density and differentiation markers (CD19, CD7, CD34).
Trajectory analysis in temporal and spectral domains yields four descriptor groups: spectral properties (power spectra, entropy, relaxation times); viscoelastic parameters inferred from fluctuation dynamics (effective stiffness, viscosity, diffusion); nonequilibrium activity indicators (Kullback–Leibler divergence, volatility, thermodynamic uncertainty, waiting-time variance); and spatial coherence metrics describing mechanical organization within the nucleus.
Together these descriptors define the nuclear mechanome, enabling single-cell characterization and patient-level signatures, and linking nuclear mechanical heterogeneity with functional biological variability.
