자료유형  

 학위논문

Control Number  

0016931466

International Standard Book Number  

9798379781316

Dewey Decimal Classification Number  

575

Main Entry-Personal Name  

Barrett, Alec.

Publication, Distribution, etc. (Imprint  

[S.l.] : Yale University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(276 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

General Note  

Advisor: Hammarlund, Marc.

Dissertation Note  

Thesis (Ph.D.)--Yale University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Nervous systems are complex networks which require a diverse set of neurons to detect, process, learn from, and respond to external stimuli. This neuronal diversity is directly tied to diversity of gene expression across the nervous system. Here I present my work, as part of the CeNGEN consortium, mapping gene expression across the entire nervous system in the hermaphrodite nematode C. elegans. I outline a strategy for sequencing RNA from individual neurons using bulk RNA-seq after cell sorting, and demonstrate a species-specific reagent for ribosomal depletion in ultra-low input RNA-seq library preparation. I describe a collaborative effort to sequence cells from all 118 neuron classes in the L4 C. elegans using single cell RNA-seq (scRNA-seq), and key findings for gene regulation, sensory modalities, and genetic drivers of functional connectivity. I describe a complementary bulk RNA-seq dataset for 41 neuron classes, and detail a computational method for integrating bulk RNA-seq data with matched scRNAseq replicates that reduces false positives in gene detection and differential gene expression. I also characterize a noncoding RNA map for the 41 neurons in the bulk RNA-seq dataset, many of which are not detected using scRNA-seq techniques. Further leveraging the combined power of scRNA-seq and bulk approaches, I demonstrate a flexible iterative subtraction method for removing contaminating counts from bulk RNA-seq datasets using an scRNA-seq reference, and show that it improves gene detection beyond current cell-type specific expression tools. Building on previous studies of scRNA-seq detection rates, I describe a simple model for denoising aggregated pseudobulk replicates using the proportion of cells that detect a gene, and show that it provides a robust measure for differential gene expression analysis, with fewer false positives. Finally, I show how integrating bulk RNA-seq after subtraction with denoised scRNA-seq pseudobulk replicates marries the strengths of both approaches, and improves accuracy in gene detection and differential expression across neuron types in C. elegans. These findings provide important new tools and strategies for linking gene expression to neuronal shape and function.

Subject Added Entry-Topical Term  

Genetics.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Molecular biology.

Index Term-Uncontrolled  

Nervous systems

Index Term-Uncontrolled  

Neurons

Index Term-Uncontrolled  

C. elegans

Index Term-Uncontrolled  

CeNGEN consortium

Index Term-Uncontrolled  

Single cell RNA-seq

Added Entry-Corporate Name  

Yale University Genetics

Host Item Entry  

Dissertations Abstracts International. 85-01B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642488