Lighting the Way: Markov Chain Monte Carlo Developed for Single Molecule Imaging

Description

Non-Small Cell Lung Cancer (NSCLC) currently represents over 80% of lung cancer cases worldwide (Medscape, 2024). The progression of NSCLC is influenced by genetic mutations in the Epidermal Growth Factor Receptors (EGFRs), which can result in unpredictable changes in their clustering on the cell membrane, crucial to determining the fate of the cell.

Fluorescence Localisation Imaging with Photobleaching (FLImP) (Iyer et. al., 2024) is a single molecule imaging technique capable of resolving EGFR molecules as close together as 3nm and determining signatures of molecular clustering at this resolution. Fluorophores, bound to the EGFRs on the cell surface, emit light which can be imaged and analysed over time. However, due to the proximity of the fluorophores, the emitted intensities’ point spread functions (PSFs) overlap, creating a single spot of their combined intensities alongside background noise, complicating individual identification. To overcome this, FLImP relies on the sequential photobleaching of the fluorescent molecules, known as fluorophores, bound to the EGFRs on the cell surface. This photobleaching, alongside short-lived ‘off’ states known as blink and dark states, generates a one-dimensional light intensity profile with a distinct staircase-like pattern. Modelling these photobleaching and off-state events can be treated as a changepoint problem andcan be used to estimate the number of fluorophores active in each observation frame.

Our research explores the novel development of reversible jump Markov chain Monte Carlo for changepoint detection to efficiently and effectively model the number of active fluorophores present in each one-dimensional frame (Green, 1995). A simulation study will be presented to validate and compare our method with the current state of the art techniques along with an application to the one-dimensional FLImP tracks, derived from two-dimensional fluorescence microscopy pixel images. We will also highlight developments within our research to uncover the number of active fluorophores directly from these images. This information can be used to estimate the separations between the fluorescently labelled EGFR clusters to determine the most likely type of EGFR clusters present and thus obtain deeper insights into the molecular processes that contribute to the development of cancer.

“Non-Small Cell Lung Cancer (NSCLC)” Medscape, 19 Mar. 2024, https://emedicine.medscape.com/article/279960-overview?form=fpf

Iyer, R. Sumanth, et al. “Drug-resistant EGFR mutations promote lung cancer by stabilizing interfaces in ligand-free kinase-active EGFR oligomers.” Nature Communications 15 (2024): 2130

Green, Peter J. "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination." Biometrika 82.4 (1995): 711-732.

Period	10 Dec 2024
Event title	32nd International Biometric Conference (IBC)
Event type	Conference
Conference number	32
Location	Georgia, United StatesShow on map
Degree of Recognition	International