Topological Inference for Organoids

Abstract

The reproducibility of organ morphology and the extent to which computational models can predict morphogenesis remain difficult to quantify, particularly for organs with complex networks of fluid-filled lumina. Here, we combine Topological Data Analysis (TDA), biophysical simulation, and Bayesian inference to study lumen morphogenesis in pancreatic organoids. Lumen formation is governed by physical processes that are challenging to measure directly, including cell proliferation and luminal osmotic pressure. We simulate organoid development using a phase-field model and address the inverse problem of inferring these parameters from either time-lapse images or single morphological snapshots. Since lumen architectures vary substantially in size, structure, and connectivity, conventional geometric descriptors provide only a partial representation of their morphology. We therefore represent each organoid using SampEuler, a topological descriptor derived from the Euler Characteristic Transform (ECT). We first show that SampEuler captures morphological information encoded by established morphometrics. We then perform parameter inference using an approximate Bayesian computation (ABC) rejection framework with the SampEuler Wasserstein distance. Using synthetic organoids with known ground-truth parameters, our approach accurately recovers the osmotic pressure and the proliferation rate while revealing a compensatory trade-off between the two processes. Applied to experimental data from ten pancreatic organoids, the inferred posterior distributions are consistent with biological expectations. Together, these results establish a non-destructive, image-based pipeline for estimating otherwise inaccessible physical parameters governing lumen formation and highlight the potential of topological representations for linking complex biological morphology to mechanistic models.

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BibTeX RIS

BibTeX

@misc{yang2026topological,
  title = {{Topological Inference for Organoids}},
  author = {Haochen Yang and Byung Ho Lee and Anne Grapin-Botton and Heather A. Harrington and Helen M. Byrne},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.27668},
  eprint = {2609.27668},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.27668},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Topological Inference for Organoids
AU  - Haochen Yang
AU  - Byung Ho Lee
AU  - Anne Grapin-Botton
AU  - Heather A. Harrington
AU  - Helen M. Byrne
PY  - 2026
DA  - 2026/09/23
PB  - arXiv
DO  - 10.48550/arxiv.2609.27668
UR  - https://arxiv.org/abs/2609.27668
AB  - The reproducibility of organ morphology and the extent to which computational models can predict morphogenesis remain difficult to quantify, particularly for organs with complex networks of fluid-filled lumina. Here, we combine Topological Data Analysis (TDA), biophysical simulation, and Bayesian inference to study lumen morphogenesis in pancreatic organoids. Lumen formation is governed by physical processes that are challenging to measure directly, including cell proliferation and luminal osmotic pressure. We simulate organoid development using a phase-field model and address the inverse problem of inferring these parameters from either time-lapse images or single morphological snapshots. Since lumen architectures vary substantially in size, structure, and connectivity, conventional geometric descriptors provide only a partial representation of their morphology. We therefore represent each organoid using SampEuler, a topological descriptor derived from the Euler Characteristic Transform (ECT). We first show that SampEuler captures morphological information encoded by established morphometrics. We then perform parameter inference using an approximate Bayesian computation (ABC) rejection framework with the SampEuler Wasserstein distance. Using synthetic organoids with known ground-truth parameters, our approach accurately recovers the osmotic pressure and the proliferation rate while revealing a compensatory trade-off between the two processes. Applied to experimental data from ten pancreatic organoids, the inferred posterior distributions are consistent with biological expectations. Together, these results establish a non-destructive, image-based pipeline for estimating otherwise inaccessible physical parameters governing lumen formation and highlight the potential of topological representations for linking complex biological morphology to mechanistic models.
ER  -

Abstract

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