Abstract
Defining the oxygen level that induces cell death within 3-D tissues is vital for understanding tissue hypoxia; however, obtaining accurate measurements has been technically challenging. In this study, we introduce a noninvasive, high-throughput methodology to quantify critical survival partial oxygen pressure (pO(2)) with high spatial resolution within spheroids by using a combination of controlled hypoxic conditions, semiautomated live/dead cell imaging, and computational oxygen modeling. The oxygen-permeable, micropyramid patterned culture plates created a precisely controlled oxygen condition around the individual spheroid. Live/dead cell imaging provided the geometric information of the live/dead boundary within spheroids. Finally, computational oxygen modeling calculated the pO(2) at the live/dead boundary within spheroids. As proof of concept, we determined the critical survival pO(2) in two types of spheroids: isolated primary pancreatic islets and tumor-derived pseudoislets (2.43 ± 0.08 vs. 0.84 ± 0.04 mmHg), indicating higher hypoxia tolerance in pseudoislets due to their tumorigenic origin. We also applied this method for evaluating graft survival in cell transplantations for diabetes therapy, where hypoxia is a critical barrier to successful transplantation outcomes; thus, designing oxygenation strategies is required. Based on the elucidated critical survival pO(2), 100% viability could be maintained in a typically sized primary islet under the tissue pO(2) above 14.5 mmHg. This work presents a valuable tool that is potentially instrumental for fundamental hypoxia research. It offers insights into physiological responses to hypoxia among different cell types and may refine translational research in cell therapies.NEW & NOTEWORTHY Our study introduces an innovative combinatory approach for noninvasively determining the critical survival oxygen level of cells within small cell spheroids, which replicates a 3-D tissue environment, by seamlessly integrating three pivotal techniques: cell death induction under controlled oxygen conditions, semiautomated imaging that precisely identifies live/dead cells, and computational modeling of oxygen distribution. Notably, our method ensures high-throughput analysis applicable to various cell types, offering a versatile solution for researchers in diverse fields.