MRI-Trackable Mesenchymal Stem Cells for Oncolytic Therapy: A Clinically Feasible Dual-Labeling Protocol

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MRI-Trackable Mesenchymal Stem Cells for Oncolytic Therapy: A Clinically Feasible Dual-Labeling Protocol

Authors

Ravi, H.; Healey, D.; Damodar, N. N.; Ackerstaff, E.; Gumin, J.; Ledbetter, D.; Lang, F. F.; Shpall, E. J.; Vilchez, M. M.; Wang, S.-J.; Quarles, C.

Abstract

Non-invasive imaging of oncolytic virus-loaded mesenchymal stem cells (MSCs) could help evaluate cell trafficking and delivery in cancer therapy. Labeling MSCs with iron oxide particles (IOPs) enables in vivo tracking using magnetic resonance imaging (MRI). This study evaluated a dual-labeling strategy that loads MSCs with MegaPro IOPs and the oncolytic virus Delta-24-RGD (DNX-2401; tasadenoturev) and assessed cellular function and MRI detectability. MSCs were labeled with IOPs alone or in combination with the oncolytic virus. We compared cell viability and intracellular iron content among IOP-labeled, dual-labeled, and naive MSCs, and evaluated differentiation, invasion, and immunophenotype in naive and IOP-labeled MSCs. We evaluated phantoms containing varying numbers of IOP-labeled MSCs (9 x 103-1.5 x 105 cells) using R2-, R2*-, and R1-weighted MRI, and generated corresponding relaxation rate maps to assess concentration dependent contrast and detection sensitivity. Iron content was comparable between IOP-labeled and dual-labeled MSCs (9.2 {+/-} 2.6 and 11.6 {+/-} 1.4 pg/cell, respectively), with >91% viability across all groups. IOP-labeled MSCs retained invasion and differentiation potential. At the highest labeled-cell concentration, R2*, R2, and R1 increased approximately 4, 2.5, and 1.5 fold, respectively, relative to the agarose control. The estimated detection limits were approximately 2 x 103 cells for R2* and 6 x 103 cells for R2 and R1. These findings demonstrate that dual labeling of MSCs with IOPs and oncolytic virus preserves key cellular characteristics while providing MRI-detectable contrast, supporting further investigation of this approach for image-guided tracking of cell-based cancer therapies.

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