Validation of genetically encoded sensors to measure intracellular potassium and metabolism in neurons

Groschup, Bernhard (2024): Validation of genetically encoded sensors to measure intracellular potassium and metabolism in neurons. Dissertation, LMU München: Graduate School of Systemic Neurosciences (GSN)

Abstract

Biological processes are highly dynamic throughout all levels of organization, ranging from the molecular level up to the full organism. To understand these dynamic processes, they can be observed with an array of different analytical methods, each with particular advantages and limitations. Since their emergence, genetically encoded sensors quickly became the method of choice for many researchers due to their excellent spatial and temporal resolution and the minimally invasive nature of these sensors. In addition, the ever-growing palette of available sensors allows measurements of a wide range of different analytes. However, genetically encoded sensors also have some key limitations, such as sub-optimal affinity, off-target recognition and environmental sensitivity. Therefore, careful interpretation of results obtained with genetically encoded sensors is warranted and potential confounding factors need to be considered in order to avoid artifactual results. Both studies discussed in this thesis address shortcomings of genetically encoded sensors and how to mitigate them. In study I we characterized lc-LysM-GEPII 1.0, a genetically encoded sensor for potassium ions (K+), with respect to its capacity to resolve neuronal K+ changes upon neuronal activity. lc-LysM-GEPII 1.0 was unable to resolve small K+ dynamics during spontaneous neuronal activity, likely because it might be saturated at physiological K+ concentrations, but reliably detected more pronounced K+ decreases during strong, tetanic activity evoked by application of Bicuculline. We confirmed these results in vivo by fluorescence lifetime imaging of lc-LysM-GEPII 1.0 in the cortex of living mice. We could not observe lifetime changes at baseline, but peri-infarct depolarizations induced by occlusion of the middle cerebral artery led to strong increases in the fluorescence lifetime of lc-LysM-GEPII 1.0. We conclude that lc-LysM-GEPII 1.0 is able to resolve K+ dynamics upon strong neuronal activity but needs to be improved with respect to affinity and dynamic range to measure responses elicited by milder stimulation. To aid this development, we developed an optogenetic stimulation approach that allowed us to titrate the sensitivity of lc-LysM-GEPII 1.0 and will help to compare the performance of different sensor variants. In study II we developed a novel method to assess the pH sensitivity of genetically encoded sensors without the need for prior purification of the sensor protein. Study II initially aimed to investigate neuronal energy metabolism within the context of GABAergic inhibition. Upon application of GABA to primary cultured neurons, we observed an increase of the FRET ratio of the lactate sensor Laconic, which was mediated by efflux of bicarbonate ions through GABAA receptors, leading to an acidification of the cytosol. While pH changes can lead to artifactual signal changes of genetically encoded sensors, pH can also act as a second messenger eliciting physiological alterations of the levels of the analyte of interest. Therefore, a signal of a genetically encoded sensor can be an artifact, a real change of analyte levels, or a combination of both. We developed a cost-effective and easy-to-use method to separate the pH sensitivity of genetically encoded sensors from the pH-induced physiological analyte response by PFA fixation. This approach, which we call Dead Cell Imaging, preserves sensor fluorescence while stopping all physiological processes. Using this method, we confirmed that the signal change of Laconic upon GABA application is a pH artifact. Furthermore, Dead Cell Imaging provides temporal information about the pH sensitivity of a genetically encoded sensor, which can help to identify complex pH artifacts and is not resolved by canonical methods of addressing the pH sensitivity of a sensor.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
• Fakultäten: Graduate School of Systemic Neurosciences (GSN)
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 21. Mai 2024
• 1. Bericht­erstatter:in: Plesnila, Nikolaus