Developing computational and experimental tools to understand chromatin dynamics and regulation
Abstract/Contents
- Abstract
- The human genome is condensed yet accessible, allowing cells to execute complex logic needed for cellular programs. The dynamic physical organization of the genome is connected to its instantaneous transcriptional profile. Current sequencing technologies can only recover a static and highly-averaged picture of these dynamic processes. Identification of dynamic signatures from chromatin motion requires an imaging and analysis platform for simultaneous tracking of genes and their transcriptional state in live cells. Advances in microscopy have provided a great opportunity to obtain such information. Unfortunately, segmenting overlapping nuclei is still a major challenge for single cell microscopic measurement. We first reported a deep-learning based model 'Nuclear Segmentation Tool' (NuSeT) that accurately segments multiple types of nuclei from imaging data. Using a hybrid architecture that consists of U-Net and Region Proposal Networks (RPN), followed by watershed processing, NuSeT has achieved superior performance in delineating nuclear boundaries in images of varying complexities. NuSeT further improves nuclear detection and reduces false positives by employing foreground normalization and additional training on synthetic images containing non-cellular artifacts. NuSeT also addresses common bottlenecks in nuclear segmentation such as limited training samples, variability in nuclear signal and shapes, and sample preparation artifacts. NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei compared with other state-of-the-art segmentation models. Next, we developed an imaging platform for simultaneous real-time visualization of locus dynamics and transcriptional output. Using a CRISPR/Cas9 based gene knock-in strategy, we have introduced MS2 hairpin cassettes into native genes of interest, which can reveal their transcriptional states. To track these loci for extended periods of time at high spatiotemporal resolution, we have generated optical tag ArrayG/N fusions of the nuclease deactivated Cas9 (dCas9) and a polycistronic cassette of repeating tRNA-sgRNA delivered by lentivirus. This platform enables sustainable tracking of non-repetitive genomic loci. We have further modified ArrayG/N tags to recruit chromatin effectors at selected genomic sites that makes this experimental platform particularly well suited for dynamic monitoring of epigenetic manipulations on selected genes. Using this platform, we have measured dynamic signatures of chromatin accessibility. We have also shown that the motion of gene loci are further constrained when they are transcriptionally active. These image analysis and cellular engineering tools can be used to answer other outstanding questions about the chromatin and gene regulation dynamics
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yang, Linfeng |
|---|---|
| Degree supervisor | Liphardt, Jan |
| Thesis advisor | Liphardt, Jan |
| Thesis advisor | Bintu, Lacramioara |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Spakowitz, Andrew James |
| Degree committee member | Bintu, Lacramioara |
| Degree committee member | Covert, Markus |
| Degree committee member | Spakowitz, Andrew James |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Linfeng Yang |
|---|---|
| Note | Submitted to the Department of Bioengineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Linfeng Yang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...