Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning

Abstract

A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, whether they are unconstrained therapeutic transplants, migrating cells during tumor metastasis, or cell populations induced by an immunological response, the role of oxygen in their fate and function is known to be pivotal albeit not well understood in vivo. To address such a challenge in the case of generation of a bioartificial pancreas, we have combined fluorine magnetic resonance imaging and unsupervised machine learning to monitor over time the spatial arrangement and the oxygen content of implants encapsulating pancreatic islets that are unconstrained in the intraperitoneal (IP) space of healthy and diabetic mice. Statistically significant trends in the postimplantation temporal dependence of oxygen content between aggregates of 0.5-mm or 1.5-mm alginate microcapsules were identified in vivo by looking at their dispersity as well as arrangement in clusters of different size and estimating oxygen content on a pixel-by-pixel basis from thousands of 2D images. Ultimately, we found that this dependence is stronger for decreased implant capsule size consistent with their tendency to also induce a larger immunological response. Beyond the bioartificial pancreas, this work provides a framework for the simultaneous spatiotemporal tracking and oxygen sensing of other cell populations and biomaterials that change over time to better understand and improve therapeutic design across diverse applications such as cellular transplant therapy, treatments preventing metastatic formation, and modulators for improving immunologic response, for all of which oxygen is a major mechanistic component. Keywords: magnetic resonance imaging; oxygen sensing; cellular therapy; diabetes; implants