Title:
Spatiotemporal Bioinformatic Analysis of Stem Cells by Spatial Genomics
Spatiotemporal Bioinformatic Analysis of Stem Cells by Spatial Genomics
Author(s)
Abramowitz, Ryan
Advisor(s)
Coskun, Ahmet F.
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
This research discusses how spatiotemporal analysis of single cells can lead to a deeper understanding of stem cell biology. Spatiotemporal analysis is the analysis of cells on the single cell level over time and space. One benefit of spatiotemporal analysis is that it can take into account heterogeneity of cells. Novel computational pipelines to aid in the spatiotemporal analysis of cells are presented and used on mesenchymal stem cells (MSCs). The current methods for generating masks of cells (AIs) are inadequate, so a new method is presented that is capable of detecting a single cell in the image. Additionally, a new algorithm to approximate differentiation time from RNA Seq data was developed. Right now, there are few algorithms to estimate differentiation states from any subset of the transcriptome, and even fewer that can accommodate missing data. The algorithm provided has some evidence that it can do both. Additionally, results from a cytokine assay are analyzed. These pipelines are also used to discuss the distribution of RNA within cell populations, and potential biological reasons for their distribution. Finally, the cellular distribution of CCL11 is analyzed alongside the cytokine assay data. A hypothesis on why the cellular distribution of CCL11 was found to be localized at the cell membrane is presented.
Sponsor
Date Issued
2021-05-04
Extent
Resource Type
Text
Resource Subtype
Thesis