No abstract

Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.

Genetically encoded sensors provide unique advantages for monitoring biological analytes with molecular and cellular-level specificity. While sensors derived from fluorescent proteins represent staple tools in biological imaging, these probes are limited to optically accessible preparations owing to physical curbs on light penetration. In contrast to optical methods, magnetic resonance imaging (MRI) may be used to noninvasively look inside intact organisms at any arbitrary depth and over large fields of view. These capabilities have spurred the development of innovative methods to connect MRI readouts with biological targets using protein-based probes that are in principle genetically encodable. Here, we highlight the stateof-the-art in MRI-based biomolecular sensors, focusing on their physical mechanisms, quantitative characteristics, and biological applications. We also describe how innovations in reporter gene technology are creating new opportunities to engineer MRI sensors that are sensitive to dilute biological targets.