All-Optical High-Throughput Functional Connectivity Mapping using Advanced Microscopy and Optogenetic Tools We propose an innovative and translational approach to map functional connections between cells in neuronal populations. Connectivity maps are the fundamental step to analyze neuronal networks. Traditionally, such maps were established by electrically recording from pairs of neurons by means of micropipettes. More recently, multi-patch protocols have been used, requiring complicated equipment and highly skilled experimenters. High-resolution connectomes are presently established by advanced histological techniques, involving serial sectioning and electron microscopy, however, this approach primarily produces anatomical maps and identification of functional connections remains difficult. Optogenetic tools and two-photon microscopy have dramatically changed functional connectivity mapping. For example, neurons expressing light-activated ion channels can be optically depolarized above their spiking threshold, and postsynaptic signals can be monitored in connected cells. Initially, hybrid approaches were taken, activating presynaptic neurons optically and measuring postsynaptic signals by whole-cell recording. More recently, all-optical mapping methods have been explored;combining light-activated channels to optically evoke activity and optical indicators to monitor activity. However, when using an all-optical approach to generate functional connectivity maps, two main challenges arise: Firstly, activating individual presynaptic neurons will produce hard to detect optical signals in postsynaptic cells as these sub-threshold postsynaptic potentials do not generate spiking. Secondly, concurrent optical stimulation and recording usually requires two separate excitation wavelengths and thus two costly lasers. Fortunately, both challenges can be met. Building on our expertise in advanced optical imaging, we will utilize 3D laser scanning technology developed in our lab and successfully applied by many research groups. To reliably detect single synaptic connections, we will optically activate presumed postsynaptic cells just at firing threshold and presumed pre- synaptic cells well above threshold. Keeping postsynaptic cells at 50% firing probability will result in readily detectable optical signals, maximizing the sensitivity for both discriminating excitatory and inhibitory connections. For concurrent stimulation and recording, we will take advantage of the small two-photon excitation volume. While this effect was seen as an obstacle to recruit sufficient light- activated channels for supra threshold stimulation, we will utilize it to employ a single wavelength to independently stimulate by scanning illumination of cell bodies and record by single-point illumination. Overall, the proposed protocol for determining functional connections by pure optical means is ideally suited for high-throughput functional connectomics. The combined use of multi-photon excitation and 3D laser scanning makes the translation from brain slices to in vivo cortex straightforward.

Public Health Relevance

In order to understand how groups of nerve cells work together in the healthy and diseased brain, we must know how these cells are connected. Mapping connections of nerve cells is complicated by their small size and large number. We propose to develop high-throughput methods to reveal the connections between living nerve cells by means of genetic engineering and advanced laser technology.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21EB016223-01A1
Application #
8582420
Study Section
Special Emphasis Panel (NOIT)
Program Officer
Conroy, Richard
Project Start
2013-07-01
Project End
2015-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
1
Fiscal Year
2013
Total Cost
$229,270
Indirect Cost
$79,270
Name
Baylor College of Medicine
Department
Neurosciences
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil et al. (2016) Benchmarking Spike Rate Inference in Population Calcium Imaging. Neuron 90:471-82
Jiang, Xiaolong; Shen, Shan; Cadwell, Cathryn R et al. (2015) Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350:aac9462