Back

Magnetic torque-driven living microrobots for enhanced tumor infiltration

Gwisai, T.; Mirkhani, N.; Christiansen, M. G.; Nguyen, T. T.; Ling, V.; Schuerle, S.

2022-01-04 bioengineering
10.1101/2022.01.03.473989 bioRxiv
Show abstract

Bacterial microrobots combining self-propulsion and magnetic guidance are increasingly recognized as promising drug delivery vehicles for targeted cancer therapy. Thus far, control strategies have either relied on poorly scalable magnetic field gradients or employed directing magnetic fields with propulsive forces limited by the bacterial motor. Here, we present a magnetic torque-driven actuation scheme based on rotating magnetic fields to wirelessly control Magnetospirillum magneticum AMB-1 bearing versatile liposomal cargo. We observed a 4-fold increase in conjugate translocation across a model of the vascular endothelium and found that the primary mechanism driving this increased transport is torque-driven surface exploration at the cell interface. Using spheroids as a 3D tumor model, fluorescently labeled bacteria colonized their core regions with up to 21-fold higher signal in samples exposed to rotating magnetic fields. In addition to enhanced transport, we demonstrated the suitability of this magnetic stimulus for simultaneous actuation and inductive detection of AMB-1. Finally, we demonstrated that RMF significantly enhances AMB-1 tumor accumulation in vivo following systemic intravenous administration in mice. Our findings suggest that scalable magnetic torque-driven control strategies can be leveraged advantageously with biohybrid microrobots. One-Sentence SummaryMagnetic torque-driven motion enhances infiltration of living microrobots across physiological barriers both in vitro and in vivo.

Matching journals

The top 8 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.