Non-Invasive metabolic profiling in tissue engineering and innate immunology by 2P-FLIM - Neto, Nuno Guilherme (Trinity College Dublin. School of Engineering. Discipline of Mechanical & Manuf. Eng, 2023)
During the golden age of biochemistry from 1920s to 1960s, many of the metabolic pathways responsible for nutrient usage and energy production in humans and other organisms was defined. During this period, glycolysis, Krebs cycle, urea cycle, oxidative phosphorylation pathways and the role of ATP were uncovered. Research in this field lost momentum due to the rise of new cellular and molecular technologies in conjugation with an overall belief that most of the intermediary metabolism has already been unravelled. Interestingly, it was the ongoing exploration of cellular biology and diseases that propelled a renewed interest in cellular metabolism. Nowadays, metabolites and metabolic pathways have several functions ranging from homeostatic regulation, biosynthesis to fuel phenotypic changes and cellular functions. In addition, cellular metabolism is also becoming a promising therapeutic target of health conditions such as cancer. With this renewed interest in cellular metabolism, new technologies and methods are also being developed to infer on specific parts of cellular metabolism. Most metabolic probing methods are invasive, require large numbers of cells or are technically challenging requiring sample pre-processing. Two-photon fluorescence lifetime imaging microscopy is a technique which uses a lower number of cells, is non-invasive and is able to follow metabolic changes occurring in real-time. This microscopy technique uses cellular autofluorescence emission and fluorescence lifetime from NAD(P)H and FAD+ to determine the metabolic profile of cells, tissues and organoids. The overall goal of this thesis is to characterise the metabolic profile of human stem cells and macrophages using two photon fluorescence lifetime imaging microscopy (2P-FLIM) concomitant with advanced data and image analysis tools. The specific aims pursued to achieve this goal are: (i) modulate human stem cell metabolism using different flow velocities in a bioreactor and biochemically validate the spatial distribution of the metabolic profile; (ii) to modulate human stem cell metabolism by promoting anaplerotic metabolic pathways to trigger higher levels of osteogenic differentiation, and (iii) to metabolically profile human macrophages using a series of metabolic chemical inhibitors in order to classify human macrophages polarisation state using machine learning models. In chapter 3 of this thesis, it is demonstrated that flow velocity in bioreactors can be tuned to modulate complex tissue environments such as the perivascular stem cell niche. Here it was observed that lower flow velocities can promote an oxygen gradient from the inlet to the outlet regions of the bioreactor due to cellular respiration. Due to this oxygen gradient, cellular metabolism is restricted and thus generates a metabolic gradient from oxidative phosphorylation to glycolysis from the inlet to the outlet regions. In chapter 4, osteogenic differentiation of human stem cells promoted by osteogenic media was profiled using 2P-FLIM. It was observed that osteogenic-differentiated stem cells rely on oxidative phosphorylation and anaplerotic pathways as a source of energy and metabolite supply to fuel biosynthetic pathways. By adding lactate as an extracellular metabolite, osteogenic differentiation was upregulated, concomitant with higher dependence on anabolic pathways such as glutaminolysis. Chapter 5 details human macrophages classification using machine learning methods trained on 2P-FLIM extracted fluorescence variables. By using 2P-FLIM, fluorescence lifetime variables were measured during on-going inhibition of metabolic pathways of polarised human macrophages using metabolic enzyme chemical inhibitors. In addition, based on 2P-FLIM predictors, machine learning models were trained and used to classify human macrophages based on its polarisation state. High classification efficiency was achieved for full field-of-view images. This methodology was also applied to classify single-cell human macrophages. Overall, the results of this thesis shows that 2P-FLIM is a powerful non-invasive tool to explore cellular metabolism. Taking in account the metabolic profile of the cells imaged with 2P-FLIM, it is possible to tailor cellular metabolism by adding metabolites, inhibitors or controlling flow velocities in bioreactors. In addition, 2P-FLIM allows real-time continuous monitoring of cellular metabolism during cell culture in cells, tissues or organoids due to its non-invasive features. This allows long term undisturbed probing of cellular metabolism during longer periods of cell culture. Furthermore, 2P-FLIM fluorescence lifetime variables can then be used as predictors for machine learning algorithms.
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Keywords: Cell Metabolism; Tissue Engineering; Immunology; FLIM; Multiphoton; Machine Learning; Microscopy