Statistical analysis supports pervasive RNA subcellular localization and alternative UTR regulation
Abstract/Contents
- Abstract
- 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.
Description
| 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 |
Creators/Contributors
| 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 |
Subjects
| 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. |
| Location | https://purl.stanford.edu/vg698wy5209 |
Access conditions
- Copyright
- © 2023 by Robert Forrest Bierman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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