Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods
- 1Indian Institute of Science Education and Research Mohali, Sector 81, SAS Nagar, Mohali, Punjab, India
Abstract
A precise analysis of shape instabilities in cell-sized membrane compartments requires thorough quantification of the shape dynamics of individual components over extended periods. Herein, we have used phase-contrast microscopy to image giant unilamellar membrane vesicles (GUVs), which are widely used as model systems for cellular structures. GUVs were prepared in the fluid lamellar phase of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine with and without cholesterol dissolved within a potato starch solution. The morphological changes and budding dynamics of individual GUVs with different chemical compositions were quantified using a deep-learning based image analysis framework over extended time periods, by using phase-contrast micrographs as input data. This automated framework integrates contour-based detection of the mother vesicle with precise segmentation of daughter compartments, thereby enabling explicit identification and tracking of mother and daughter vesicles at high temporal resolution. These findings further facilitate the measurement of the temporal evolution of vesicle morphometrics including area, and local membrane curvature during budding, that can be used as an input to develop computational models aimed at elucidating curvature-driven membrane remodeling in complex soft materials.
Related
- kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters
- Toward objective brain monitoring of cancer-related cognitive impairment: an extracellular vesicle–based conceptual framework
- AI-guided design and biomaterial integration for next-generation extracellular vesicle therapeutics: an evidence-weighted translational framework.
- Replace, Reprogram, and Relay: A Framework for Glial Cell and Extracellular Vesicle Therapies in CNS Repair
- Advances in Machine Learning for Directed Evolution: A Five-Year Retrospective
BibTeX
@misc{pandey2026evolution,
title = {{Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods}},
author = {T. Pandey and R. Kudawla and S. Negi and J. B. S. Varshan and C. Devarakonda and D. Singh and A. Chatterjee and T. Bhatia},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.03.764022},
url = {https://www.biorxiv.org/content/10.64898/2026.11.03.764022v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods
AU - T. Pandey
AU - R. Kudawla
AU - S. Negi
AU - J. B. S. Varshan
AU - C. Devarakonda
AU - D. Singh
AU - A. Chatterjee
AU - T. Bhatia
PY - 2026
DA - 2026/11/03
PB - bioRxiv
DO - 10.64898/2026.11.03.764022
UR - https://www.biorxiv.org/content/10.64898/2026.11.03.764022v1
AB - A precise analysis of shape instabilities in cell-sized membrane compartments requires thorough quantification of the shape dynamics of individual components over extended periods. Herein, we have used phase-contrast microscopy to image giant unilamellar membrane vesicles (GUVs), which are widely used as model systems for cellular structures. GUVs were prepared in the fluid lamellar phase of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine with and without cholesterol dissolved within a potato starch solution. The morphological changes and budding dynamics of individual GUVs with different chemical compositions were quantified using a deep-learning based image analysis framework over extended time periods, by using phase-contrast micrographs as input data. This automated framework integrates contour-based detection of the mother vesicle with precise segmentation of daughter compartments, thereby enabling explicit identification and tracking of mother and daughter vesicles at high temporal resolution. These findings further facilitate the measurement of the temporal evolution of vesicle morphometrics including area, and local membrane curvature during budding, that can be used as an input to develop computational models aimed at elucidating curvature-driven membrane remodeling in complex soft materials.
ER -