Solaris: a panel of bright and sensitive hybrid voltage indicators for imaging membrane potential in cultured neurons
Yang, J.; Zhu, S.; Yang, L.; Peng, L.; Han, Y.; Hayward, R. F.; Park, P.; Hu, D.; Cohen, A. E.; Zou, P.
Show abstract
Dynamic changes in the membrane potential underlie neuronal activities. Fluorescent voltage indicators allow optical recording of electrical signaling across a neuronal population with cellular precision and at millisecond-level temporal resolution. Here we report the design and characterization of a chemigenetic hybrid voltage indicator, Solaris, in which a circularly permuted HaloTag is inserted into the first extracellular loop of Acetabularia rhodopsin. Solaris is compatible with fluorogenic HaloTag ligands JF525, JF549, JF552, JF585, and JF635. The most sensitive conjugate, Solaris585, has more than 2-fold higher voltage sensitivity than the spectrally similar Voltron2585 ({Delta}F/F0 = -28.1 {+/-} 1.3% versus -12.3 {+/-} 0.7% per action potential in cultured neurons). Solaris585 supports the measurement of optogenetically evoked spike activity or dual-color imaging in conjunction with green-emitting calcium or glutamate indicators. Solaris indicators are also applicable to fluorescence lifetime imaging, which probes the absolute membrane potential. This new hybrid voltage indicator is a valuable tool for imaging neuronal electrophysiological activities in cultured cells with substantially improved dynamic range compared to previous hybrid indicators.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Two-photon photoactivated voltage imaging in tissue with an Archaerhodopsin-derived reporter 96%
- Photophysics-informed two-photon voltage imaging using FRET-opsin voltage indicators 95%
- Bright and sensitive red voltage indicators for imaging action potentials in brain slices and pancreatic islets 95%
"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.