Hidden in plain light: high-resolution time-resolved fluorescence modelling of lung cancer

Abstract

Fibre-optic fluorescence lifetime-based devices are advanced spectroscopy techniques that can measure tissue autofluorescence (AF). The optical information AF offers provides insights into the tissue’s metabolic and structural composition, as well as its surrounding environment. Therefore, these devices can be used to interrogate tissue. Conventional fluorescence lifetime-based devices typically measure the AF of tissue from broad emission channels. Or where multiple high-resolution channels are measured, the individual decay traces are often averaged into a single channel. Our research uses a novel in-house device, the Extensively Parallel Time-Resolved Fluorescence Spectroscopy (EP-TRFS) device which simultaneously measures high-resolution spectral and temporal fluorescence. We investigate device specific factors within the data collection, such as the instrument response function and sample specific factors such as photobleaching (Chapter 2).

We next present the paper titled “Simultaneous Spectral Temporal Modelling for a Time-Resolved Fluorescence Emission Spectrum”, based on the Multichannel Fluorescence Lifetime Estimation (MuFLE) model (Chapter 3). MuFLE is an efficient computational model developed to explore the unique multi-channel spectroscopy data, simultaneously estimating the emission spectra and the spectral fluorescence lifetime in single and multi-exponential modes. We show the effectiveness of this approach in estimating the emission and spectral fluorescence lifetime of reference samples, and in un-mixing mixed reference samples.

We then present our initial findings of MuFLE when applied to ex vivo lung tissue data, presented in the paper titled “Fibre-optic based Exploration of Lung Cancer Autofluorescence using Spectral Fluorescence Lifetime”, exploring the spectral information single-exponential fluorescence lifetime estimation provides (Chapter 4). The study demonstrates the sensitivity of the spectral fluorescence lifetime shape to the relative concentration of underlying fluorophores, independent of their environment. This study then explores the properties of the spectral fluorescence lifetime in paired ex vivo lung tissue deemed either abnormal or normal by pathologists.

When used in a multi-exponential mode, we finally show the performance of MuFLE in un-mixing endogenous fluorophores simultaneously in both the spectral and temporal domains of ex vivo lung samples from both non-cancerous and cancerous tissue (Chapter 5). We validate the presence of specific un-mixed endogenous fluorophores, using a commercial FLIM setup of paired samples. We also validate the spectral and temporal profile of the endogenous fluorophores when measured benchside and with the expected values estimated in the literature. The identification of the fluorescence molecules responsible for AF changes in ex vivo samples, enable endogenous fluorophore specific label-free tracking. This, in turn, enhances our ability to assess individual fluorescence components contributing to the overall AF variation between non-cancerous and cancerous tissue in vivo.

In conclusion, we have developed and validated the results from a high-resolution fluorescence device, in combination with novel analytical models. This innovative approach when applied to lung tissue diagnosis enables us to gain a deeper understanding of the individual fluorescence components that contribute to the total AF of a tissue sample. By performing a comprehensive assessment of the AF, identifying the underlying sources of the individual signals, and their relative contributions becomes possible, better distinguishing fluorescence changes between cancerous and non-cancerous tissue. Consequently, the integration of this device and MuFLE, if applied in vivo, can potentially facilitate the instantaneous measurement of specific molecular and environmental properties associated with individual endogenous fluorophores in tissue. This approach can provide novel insights, offering a comprehensive understanding of the molecular dynamics in in vivo tissue, label-free and in real-time.