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Serotonergic Circuits: Role in Sleep and Enhanced Genetic Tools for Access and Optical Recording

Citation

Altermatt, Michael (2021) Serotonergic Circuits: Role in Sleep and Enhanced Genetic Tools for Access and Optical Recording. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/gfe2-w578. https://resolver.caltech.edu/CaltechTHESIS:01202021-155015733

Abstract

Overall, this thesis encompasses three main directions: the study of neural circuits in sleep (Chapter 2), the development and testing of tools for measuring neuromodulator release (Chapter 3), and methods for in vivo characterization of gene delivery vehicles (Chapter 5).

The role of the neuromodulator serotonin in sleep has been debated for over 60 years. Until recently, the serotonergic system was widely thought to be part of the arousal system and promote wakefulness. In Chapter 2, we investigate the function of serotonin-producing neurons in murine and zebrafish sleep with tools featuring superior specificity and precision compared to previously employed techniques. Our results demonstrate that the serotonergic raphe are sleep-promoting and required for sleep homeostasis. Intriguingly, serotonergic neurons in mice can have opposing effects on sleep depending on the firing mode.

The release of serotonin from neurons can be regulated by the frequency of neuronal firing and can occur at classical synapses, varicosities, soma, and dendrites. Further examination of the complex signaling mechanism of serotonin would benefit from tools capable of measuring the release of serotonin in vivo with long-term stability and high spatiotemporal resolution. To this end, we developed and characterized iSeroSnFR, an intensity-based genetically encoded serotonin indicator. In Chapter 3, we demonstrate that iSeroSnFR can detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep-wake transitions.

Adeno-associated viruses (AAVs) have been extensively used as gene delivery vehicles in basic neuroscience and gene therapy. However, optimization of transduction efficiency and target specificity remain a key challenge to overcome. Several AAV vector engineering approaches have been devised for this purpose and yield large collections of candidates that require further in vivo characterization. However, conventional characterization methods fall short with regard to in-depth cell type tropism analysis and/or high-throughput capabilities. In Chapter 5, we address this shortcoming with single-cell RNA sequencing technologies based on the Drop-seq method. We established an experimental and computational pipeline that allows us to profile the viral tropism of multiple AAV variants in parallel across numerous complex cell types.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Sleep; Dorsal raphe nucleus; Serotonin; Protein-based sensors; Adeno-associated virus; Single-cell RNA sequencing;
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Neurobiology
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Gradinaru, Viviana
Group:Gradinaru, Viviana
Thesis Committee:
  • Prober, David A. (chair)
  • Anderson, David J.
  • Hong, Elizabeth J.
  • Gradinaru, Viviana
Defense Date:16 December 2020
Record Number:CaltechTHESIS:01202021-155015733
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:01202021-155015733
DOI:10.7907/gfe2-w578
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.neuron.2019.05.038DOIThesis Chapter 2
https://doi.org/10.1016/j.cell.2020.11.040DOIThesis Chapter 3
https://doi.org/10.1016/j.ymthe.2020.04.019DOIAbstract adapted for a section of Thesis Chapter 5.
ORCID:
AuthorORCID
Altermatt, Michael0000-0003-2841-5374
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14056
Collection:CaltechTHESIS
Deposited By: Michael Altermatt
Deposited On:12 Feb 2021 16:58
Last Modified:19 Feb 2021 21:23

Thesis Files

[img] PDF (Altermatt_Michael_2021_Final) - Final Version
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9MB
[img] Video (MPEG) (Video abstract for Neuron publication) - Supplemental Material
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41MB
[img] MS Excel (Data S1. Raw Data Used for Machine Learning Analysis, Related to Figure 1.) - Supplemental Material
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4MB

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