Julie A. Cass

Cells appear extraordinarily complex, yet aspects of their organization may arise from a small number of influences acting within geometric constraints. Here, we extend and use cellPACK, an open‐source framework for rule‐based spatial modeling, to compare observed and simulated 3D distributions of peroxisomes and endosomes (puncta) in human induced pluripotent stem cells. Simulations conditioned on observed cell and nuclear geometries along with puncta counts show that geometric variation can generate apparent structure in organelle localization, highlighting the need to account for geometry when interpreting spatial statistics. We therefore employ an occupancy‐based metric that normalizes puncta density by available intracellular volume, revealing modest enrichment near the nuclear surface against an otherwise near‐uniform cytoplasmic distribution. We use these occupancy patterns to fit combinations of predefined spatial bias models and evaluate the resulting 3D packings against observed data. To support open and reproducible modeling while enabling a more intuitive visual exploration, we present cellPACK Studio , a browser‐based interface that allows users to generate, modify, and visualize the 3D cell packings used in this study (available at cellpack.allencell.org ). Together, these results and tools illustrate how spatial modeling can transform descriptive imaging data into testable, interpretable hypotheses of intracellular organization.

The epithelial to mesenchymal transition (EMT) is a widely studied but poorly defined state change due to the variety of ways in which it has been characterized in cells. There is a need for reproducible cell model systems that enable the integration and comparison of different types of measured observations of cells across many distinct cellular contexts. We present human induced pluripotent stem (hiPS) cells as such a model system by demonstrating its utility through a comparative analysis of hiPS cell-EMT in 2D and 3D cell culture geometries. We developed live-imaging-based assays to directly compare examples of changes in cell function (via migration timing), molecular components (via expression of marker proteins), organization (via reorganization of cell junctions), and environment (via dynamics of basement membrane) in the same experimental system. The EMT-related changes we measured occurred earlier in 2D colonies than in 3D lumenoids, likely due to differences in the basement membrane environments associated with 2D vs. 3D initial hiPS cell culture geometries. We have made the 449 60-hour-long 3D time-lapse movies and the associated tools used for analysis and visualization open-source and easily accessible as a resource for future work in this field.

Summary: To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

Muscle's function within an organism depends on the feedback between molecular to meter-scale processes. While the motions of muscle's contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved x-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta. Since the primary flight muscles of Manduca sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 (s.d. 26) ms following activation. In addition, the thick filaments stretched an average of 0.75 (s.d. 0.32)% and thin filaments stretched 1.11 (s.d. 0.65)%. In contrast to other in vivo preparations, lattice spacing changed an average of 2.72 (s.d. 1.47)%. Lattice dilation of this magnitude significantly impacts shortening velocity and force generation, and filament stretching tunes force generation. While kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. While lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle's adaptability and may facilitate its robust function in unpredictable conditions.

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.

Muscle’s function within an organism depends on the feedback between molecular to meter-scale processes. While the motions of muscle’s contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved x-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta . Since the primary flight muscles of Manduca sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 (s.d. 26) ms following activation. In addition, the thick filaments stretched an average of 0.75 (s.d. 0.32)% and thin filaments stretched 1.11 (s.d. 0.65)%. In contrast to other in vivo preparations, lattice spacing changed an average of 2.72 (s.d. 1.47)%. Lattice dilation of this magnitude significantly impacts shortening velocity and force generation, and filament stretching tunes force generation. While kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. While lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle’s adaptability and may facilitate its robust function in unpredictable conditions.

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.

To characterize the consequences of eliminating essential functions needed for peptidoglycan synthesis, we generated deletion mutations of Acinetobacter baylyi by natural transformation and visualized the resulting microcolonies of dead cells. We found that loss of genes required for peptidoglycan precursor synthesis or polymerization led to the formation of polymorphic giant cells with diameters that could exceed ten times normal. Treatment with antibiotics targeting early or late steps of peptidoglycan synthesis also produced giant cells. The giant cells eventually lysed, although they were partially stabilized by osmotic protection. Genome-scale transposon mutant screening (Tn-seq) identified mutations that blocked or accelerated giant cell formation. Among the mutations that blocked the process were those inactivating a function predicted to cleave murein glycan chains (the MltD murein lytic transglycosylase), suggesting that giant cell formation requires MltD hydrolysis of existing peptidoglycan. Among the mutations that accelerated giant cell formation after ß-lactam treatment were those inactivating an enzyme that produces unusual 3->3 peptide cross-links in peptidoglycan (the LdtG L,D-transpeptidase). The mutations may weaken the sacculus and make it more vulnerable to further disruption. Although the study focused on A. baylyi, we found that a pathogenic relative (A. baumannii) also produced giant cells with genetic dependencies overlapping those of A. baylyi. Overall, the analysis defines genetic pathway for giant cell formation conserved in Acinetobacter species in which independent initiating branches converge to create the unusual cells. Although essential genes control the most basic functions of bacterial life, they are difficult to study genetically because mutants lacking the functions die. We have developed a simple procedure for creating bacteria in which different essential genes have been completely deleted, making it possible to analyze the roles of the missing functions based on the features of the dead cells that result. When genes needed for the production of the cell wall were inactivated, the bacteria formed bizarre giant cells. It was possible to identify the functions responsible for forming the giant cells, and to formulate a model for how they form. Since cell wall synthesis is one of the most important antibiotic targets, understanding how bacteria respond to its disruption may ultimately help in developing procedures to overcome antibiotic resistant bacterial infections.