Back

Imaging progenitor cell differentiation during central nervous system remyelination using an MRI gene reporter

Brechbuehl, E.; Hill, M.; Chen, C.; Gaitsch, H.; Ros, S.; Porcu, L.; Wright, A.; Reich, D.; Franklin, R.; Brindle, K.

2025-09-15 neuroscience
10.1101/2025.09.10.674209 bioRxiv
Show abstract

Demyelination, the loss of the myelin sheath from around otherwise intact axons, occurs in several diseases, most notably multiple sclerosis (MS). Demyelinated axons that are not remyelinated are vulnerable to irreversible degeneration and therefore therapies that enhance remyelination have been sought. However, there remains a paucity of suitable outcome measures to assess their efficacy. Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that is used both preclinically and clinically for the assessment of anatomy and tissue function. Here we describe an MRI technique for following the differentiation of oligodendrocyte progenitor cells (OPCs) into oligodendrocytes during the spontaneous regenerative process of remyelination in vivo. OPCs were transduced in situ with a lentiviral vector expressing an organic anion transporting polypeptide (Oatp1a1) under the control of the differentiation-specific Myelin Basic Protein (MBP) promoter. Oatp1a1 mediates cell uptake of a gadolinium-based MRI contrast agent (Primovist), allowing detection of the cells in T1-weighted MR images. Uptake of the contrast agent is restricted to MBP-expressing cells, which is most highly expressed during myelin sheath formation, thereby allowing progenitor-mediated, and potentially oligodendrocyte-mediated, remyelination to be monitored non-invasively in vivo using MRI. These findings provide the foundation for the development of direct methods for assessing the efficacy of pro-remyelination therapies.

Published in Brain (predicted rank #3) · training set

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.