Statistical analysis supports pervasive RNA subcellular localization and alternative UTR regulation

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Understanding 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.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2023; ©2023
Publication date 2023; 2023
Issuance monographic
Language English


Author Bierman, Robert Forrest
Degree supervisor Salzman, Julia
Thesis advisor Salzman, Julia
Thesis advisor Harbury, Pehr
Thesis advisor Krasnow, Mark, 1956-
Degree committee member Harbury, Pehr
Degree committee member Krasnow, Mark, 1956-
Associated with Stanford University, School of Medicine
Associated with Stanford University, Department of Biochemistry


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Robert Bierman.
Note Submitted to the Department of Biochemistry.
Thesis Thesis Ph.D. Stanford University 2023.

Access conditions

© 2023 by Robert Forrest Bierman
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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