Kavli Affiliate: Fred Gage
| Authors: W. Sharon Zheng, Yajun Zhang, Roger E. Zhu, Peng Zhang, Smriti Gupta, Limeng Huang, Deepika Sahoo, Kaiming Guo, Matthew E. Glover, Krishna C. Vadodaria, Mengyao Li, Tongrui Qian, Miao Jing, Jiesi Feng, Jinxia Wan, Philip M. Borden, Kaspar Podgorski, Farhan Ali, Alex C. Kwan, Li Gan, Li Lin, Fred H. Gage, Barbara Jill Venton, Jonathan S. Marvin, Sarah M. Clinton, Miaomiao Zhang, Loren Looger, Yulong Li and J. Julius Zhu
| Summary:
Abstract Intercellular communication mediated by a large number of neuromodulators diversifies physiological actions, yet neuromodulation remains poorly understood despite the recent upsurge of genetically encoded transmitter sensors. Here, we report the development of a versatile genetically encoded sensor-based image analysis program (GESIAP) that utilizes MATLAB-based algorithms to achieve high-throughput, high-resolution processing of sensor-based functional imaging data. GESIAP enables delineation of fundamental properties (e.g., transmitter spatial diffusion extent, quantal size, quantal content, release probability, pool size, and refilling rate at single release sites) of transmission mediated by various transmitters (i.e., monoamines, acetylcholine, neuropeptides, and glutamate) at various cell types (i.e., neurons, astrocytes, and other non-neuronal cells) of various animal species (i.e., mouse, rat, and human). Our analysis appraises a dozen of newly developed transmitter sensors, validates a conserved model of restricted non-volume neuromodulatory synaptic transmission, and accentuates a broad spectrum of presynaptic release properties that variegate neuromodulation. Competing Interest Statement The authors have declared no competing interest.