Multiscale topological analysis for mapping molecular environments and dynamics

Panconi, Luca ORCID: 0000-0001-6025-8597 (2024). Multiscale topological analysis for mapping molecular environments and dynamics. University of Birmingham. Ph.D.

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Abstract

Modern fluorescence microscopy techniques can visualise biological samples from micro- to nano-scale resolutions. Conventional microscopy produces images of cellular structures, while single molecule localisation microscopy (SMLM) localises individual molecules, which are represented as spatial or marked point pattern data. Depending on the imaging target and modality, the data arising from fluorescence microscopy can vary in architecture and present features with irregular or unpredictable geometries, which can be challenging to quantify. Topological data analysis is invariant of geometry and lends itself well to this form of feature extraction, but is yet to see widespread adaptation in quantifying biophysical properties of cellular structures. The cell plasma membrane is of particular interest, as the dynamic reorganisation of transmembrane proteins plays a crucial role in regulating cell signalling. Dysfunction in this process is associated with several human disorders,
such as autoimmune diseases and cancer. Further, it is hypothesised that this reorganisation is influenced by the biophysical properties of the membrane, such as lipid composition. As such, this thesis concerns the development of novel topological data analysis techniques for feature extraction in image and point pattern data, with an emphasis on investigating membrane properties across acquisition scales. For conventional fluorescence microscopy, we present a topological image analysis tool (TOBLERONE) for cell and organelle segmentation. Then, we produce a framework for agent-based modelling of molecular aggregation on the plasma membrane (ASMODEUS), which simulates transmembrane protein dynamics. Furthermore, we introduce a software package (PLASMA) for partitioning marked point patterns and identify nano-scale lipid heterogeneity in RAMA27 SMLM data. These techniques may yield a promising avenue for mapping multiscale membrane properties.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):