Fast image and data processing methods for novel neuroscience technologies
Name
Celiker-oceliker-PhD-EECS-2022-thesis.pdf
Description
Thesis PDF
Size
9.63 MB
Format
Adobe PDF
Checksum (MD5)
fecb0841413bad6a34713dfafc089388
Author(s)
Çeliker, Orhan Tunç
Advisor(s)
Boyden, Edward S.
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
The nematode C. elegans, a transparent animal with 302 neurons, is a suitable model organism for whole-brain measurement of nervous activity. However, under panneuronal labeling, it is difficult to resolve the identity of the neurons by shape or location alone. We propose a fluorescent in situ hybridization (FISH) based pipeline for reading out gene expression from neurons. Using optimization methods, we select a compact set of genes that provide enough information to distinguish every neighboring pair of neurons in the nervous system. We show that we can process volumetric images of live and fixed C. elegans to read out the gene expression patterns of each observed neuron and match it to their calcium indicator data. Separately, we also outline computational approaches to processing fluorescence data from novel fluorescent sensors.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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