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New Biological Frontiers Illuminated by Molecular Sensors and Actuators
Poster Abstracts
41-POS
Board 41
Imaging Subcellular Voltage Dynamics in vivo with Improved Genetically Encoded
Indicators
Francois St-Pierre
, Helen H. Yang, Xiaozhe Ding, Ying Yang, Thomas R. Clandinin, Michael
Z. Lin.
Stanford University, Stanford, CA, USA.
Nervous systems encode information as spatiotemporal patterns of membrane voltage transients,
so accurate measurement of electrical activity has been of long-standing interest. Recent
engineering efforts have improved our ability to monitor membrane voltage dynamics using
genetically encoded voltage indicators. In comparison with electrophysiological approaches,
such indicators can monitor many genetically defined neurons simultaneously; they can also
more easily measure voltage changes from subcellular compartments such as axons and
dendrites. Compared with genetically encoded calcium indicators, voltage sensors enable a more
direct, accurate, and rapid readout of membrane potential changes. However, several challenges
remain for in vivo voltage imaging with genetically encoded indicators. In particular, current
voltage sensors are characterized by insufficient sensitivity, kinetics, and/or brightness to be true
optical replacements for electrodes
in vivo
.
As a first step towards addressing these challenges, we developed new voltage indicators,
ASAP2f and ASAP2s, that further improve upon the sensitivity of the fast voltage sensor
Accelerated Sensor of Action Potentials 1 (ASAP1). We also describe here how these novel
sensors are able to report stimulus-evoked voltage responses in axonal termini of the fly visual
interneuron L2. In this system, ASAP sensors enabled the monitoring of neural activity with
greater temporal resolution than three recently reported calcium and voltage sensors. Overall, our
study reports novel voltage indicators with improved performance, illustrates the importance of
sensor kinetics for accurately reporting neural activity, and suggests L2 as an
in vivo
platform for
benchmarking neural activity sensors. We anticipate that ASAP2f and ASAP2s will facilitate
current and future efforts to understand how neural circuits represent and transform information.