자료유형  

 학위논문

Control Number  

0016931986

International Standard Book Number  

9798379654320

Dewey Decimal Classification Number  

591

Main Entry-Personal Name  

Bierman, Robert Forrest.

Publication, Distribution, etc. (Imprint  

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

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(101 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Harbury, Pehr;Krasnow, Mark;Salzman, Julia.

Dissertation Note  

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

Restrictions on Access Note  

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

Summary, Etc.  

요약Understanding the subcellular localization of RNA molecules across different cell-types and tissues provides a window into previously unknown biology and disease. Low-plex RNA imaging technologies, where only a handful of RNA species could be observed in a single experiment, have been transformed by novel spatially resolved imaging and sequencing techniques which can simultaneously investigate thousands of genes. Named "Method of the Year" in 2019 by Nature Methods, the field of spatially resolved transcriptomics continues to accelerate in resolution, sensitivity, and ease of use. Large and exciting datasets have been produced from these efforts, but have been underutilized to discover subcellular RNA localization.We introduce a novel statistical framework to identify RNA subcellular localization patterns in publicly available datasets. We detect that a majority of investigated genes have non-random RNA distribution, and often differential distribution between cell-types.We've combined our analyses of spatial datasets with standard, spatially-naive, single-cell RNA sequencing to further identify genes which have subcellular localization patterning correlated with RNA isoform usage to generate testable hypotheses which we've collaborated with others to successfully validate.Spatial Transcriptomics is rapidly evolving, and we expect that our contribution of a flexible and statistically-sound algorithm will be applicable for the impending influx of spatial datasets.

Subject Added Entry-Topical Term  

Embryos.

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Neurons.

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Cytoskeleton.

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Regulation.

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Fibroblasts.

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Binding sites.

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Microscopy.

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Medical research.

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Insects.

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Localization.

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Genes.

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Dissection.

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Morphology.

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Motility.

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Cell culture.

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Cellular biology.

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Medicine.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:643222