Geometry‐aware modeling with cellPACK for evaluating spatial trends in intracellular organization
- 1Allen Institute for Cell Science Seattle Washington USA
- 2Department of Biology and Howard Hughes Medical Institute University of Washington Seattle Washington USA
Abstract
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.
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BibTeX
@article{mogre2026geometryaware,
title = {{Geometry‐aware modeling with cellPACK for evaluating spatial trends in intracellular organization}},
author = {Saurabh S. Mogre and Ruge Li and Allison Scibisz and Thao P. Do and Julie C. Dixon and Matheus P. Viana and Ehssan Nazockdast and Susanne M. Rafelski and Julie A. Theriot and Megan M. Riel‐Mehan and Graham T. Johnson},
journal = {Protein Science : A Publication of the Protein Society},
year = {2026},
month = oct,
doi = {10.1002/pro.70832},
url = {https://doi.org/10.1002/pro.70832},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Geometry‐aware modeling with cellPACK for evaluating spatial trends in intracellular organization
AU - Saurabh S. Mogre
AU - Ruge Li
AU - Allison Scibisz
AU - Thao P. Do
AU - Julie C. Dixon
AU - Matheus P. Viana
AU - Ehssan Nazockdast
AU - Susanne M. Rafelski
AU - Julie A. Theriot
AU - Megan M. Riel‐Mehan
AU - Graham T. Johnson
PY - 2026
DA - 2026/10/27
JO - Protein Science : A Publication of the Protein Society
DO - 10.1002/pro.70832
UR - https://doi.org/10.1002/pro.70832
AB - 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.
SN - 0961-8368
SN - 1469-896X
ER -