Point-of-care optical spectroscopy platform and ratio-metric algorithms for rapid and systematic functional characterization of biological models in vivo

Significance: Cellular metabolism is highly dynamic and strongly influenced by its local vascular microenvironment, gaining a systems-level view of cell metabolism in vivo is essential in understanding many critical biomedical problems in a broad range of disciplines. However, very few existing metabolic tools can quantify the major metabolic and vascular parameters together in biological tissues in vivo with easy access.

Aim: We aim to fill the technical gap by demonstrating a point-of-care, easy-to-use, easy-to-access, rapid, systematic optical spectroscopy platform for metabolic and vascular characterizations on biological models in vivo to enable scientific discoveries to translate more efficiently to clinical interventions.

Approach: We developed a highly portable optical spectroscopy platform with a tumor-sensitive fiber probe and easy-to-use spectroscopic algorithms for multi-parametric metabolic and vascular characterizations of biological tissues in vivo. We then demonstrated our optical spectroscopy on tissue-mimicking phantoms, human subjects, and small in vivo tumor models. We also validated the proposed easy-to-use algorithms with the Monte Carlo inversion models for accurate and rapid spectroscopic data processing.

Results: Our tissue-mimicking phantom, human subjects, and in vivo animal studies showed that our portable optical spectroscopy along with the new spectroscopic algorithms could quantify the major metabolic and vascular parameters on biological tissues with a high accuracy. We also captured the highly diverse metabolic and vascular phenotypes of head and neck tumors with different radiation sensitivities.

Conclusions: Our highly portable optical spectroscopy platform along with easy-to-use spectroscopic algorithms will provide an easy-to-access way for rapid and systematic characterizations of biological tissue metabolism and vascular microenvironment in vivo, which may significantly advance translational cancer research in the future.