Vasquez, P. A. | Search bioRxiv

bioRxiv2025

Stochastic Modeling of Hematopoietic Stem Cell Dynamics

The study of hematopoietic stem cell (HSCs) maintenance and differentiation to supply the hematopoietic system presents unique challenges, given the complex regulation of the process and the difficulty in observing cellular interactions in the stem cell niche. Quantitative methods and tools have emerged as valuable mechanisms to address this issue; however, the stochasticity of HSCs presents significant challenges for mathematical modeling, especially when bridging the gap between theoretical models and experimental validation. In this work, we have built a flexible and user-friendly stochastic dynamical and spatial model for long-term HSCs (LT-HSCs) and short-term HSCs (ST-HSCs) that captures experimentally observed cellular variability and heterogeneity. Our model implements the behavior of LT-HSCs and ST-HSCs and predicts their homeostatic dynamics. Furthermore, our model can be modified to explore various biological scenarios, such as stress-induced perturbations mediated by apoptosis, and successfully implement these conditions. Finally, the model incorporates spatial dynamics, simulating cell behavior in a 2D environment by combining Brownian motion with spatially graded parameters. *Summary StatementThis study addresses the challenge of characterizing hematopoietic stem cell (HSC) dynamics by developing a flexible, user-friendly stochastic spatial model of long-term and short-term HSCs. The model captures observed cellular variability and heterogeneity, predicts homeostatic dynamics, can be adapted to simulate stress-induced perturbations like apoptosis, and incorporates a spatial component to analyze HSC movement within a bone marrow niche.

developmental biology↗

bioRxiv2024

Single Particle Tracking of Genetically Encoded Nanoparticles: Optimizing Expression for Cytoplasmic Diffusion Studies

Single particle tracking (SPT) is a powerful technique for probing the diverse physical properties of the cytoplasm. Genetically encoded nanoparticles provide an especially convenient tool for such investigations, as they can be expressed and tracked in cells via fluorescence. Among these, 40-nm GEMs provide a unique opportunity to explore the cytoplasm. Their size corresponds to that of ribosomes and big protein complexes, allowing us to investigate the effects of the cytoplasm on the diffusivity of these objects while excluding the influence of chemical interactions during stressful events and pathological conditions. However, it has been shown that cytoplasmic viscosity is tightly regulated and plays a crucial role in maintaining homeostasis during protein synthesis and degradation. Despite this, the effects of GEM expression levels on diffusivity remain largely uncharacterized in mammalian cells. To optimize the GEMs tracking and estimate GEMs-expression effects we constructed dox-inducible GEM expression system and compare with a previously reported constitutive expression system. The optimized level of GEMs expression increases the measured diffusivity from 0.29 {+/-} 0.02 m2/sec in GEMs-overexpressed cells to 0.35 {+/-} 0.02 m2/sec; improve homogeneity throughout the cell population; and facilitates particle tracking. We also improved the analyses of GEM diffusivity by applying effective diffusion coefficient while considering the type of motion and assessing the heterogeneity in the type of motion by calculating the standard deviations of particle displacements. Statement of significanceDescribing cytoplasmic properties, such as environmental viscosity and protein complex motion, is essential for understanding molecular-level changes in cell function and pathology. A recently developed approach uses self-assembling fluorescent protein probes, expressed in cells, to investigate cytoplasmic properties through single-particle tracking (SPT). One such system employs genetically encoded multimeric (GEM) nanoparticles-- scaffold protein structures similar in size to ribosomes. This study addresses a key limitation in SPT of GEMs by examining how varying GEM expression levels affect measured diffusivity and tracking quality in mammalian cells. Our findings demonstrate that controlled GEM expression reduces particle overcrowding, increases measured diffusivity, and enhances track detection. This work contributes valuable insights into optimizing GEM nanoparticle applications for studying cytoplasmic viscosity and motion dynamics.

biophysics↗