Systematic characterization of gene regulatory interactions with high-throughput CRISPR screens
Abstract/Contents
- Abstract
- Chapter 1: Hundreds of thousands of cis-regulatory elements (CREs) have been identified across a myriad of cell types and conditions, but relatively few have been empirically characterized. Here, we present a scalable, phenotype-driven CRISPR screen to identify hundreds of essential regulatory elements in K562 cells by perturbing 7,500 DNase hypersensitive sites (DHS) around the top 400 essential genes in K562. In addition to distal enhancers, we identify promoter-overlapping enhancers that selectively regulate multiple genes. Using orthogonal CRISPR systems with narrow or broad perturbative windows, we demonstrate that fine-mapping screens can resolve and characterize core transcription factor binding sites (TFBSs) within enhancers, highlighting the advantages of complementary CRISPR systems to investigate CREs. Lastly, we use our results to further interrogate how transcription factor binding sites hierarchically interact to drive enhancer function at the CBFA2T3 locus. This study demonstrates a scalable CRISPR screening framework for identifying and dissecting CREs that regulate selectable phenotypes. Chapter 2: The ENCODE consortium's encyclopaedia, containing millions of non-coding CREs has advanced our understanding of gene regulatory landscapes. Nonetheless, few CREs have been functionally characterized directly; determining the regulatory effects on gene expression is a critical next step, and pooled, non-coding CRISPR screens have emerged as a valuable tool to endogenously characterize the function of CREs. However, these screens' design, execution, and analysis have been highly heterogeneous. Here, we present the ENCODE CRISPR screen database: the largest, multi-center, standardized collection of non-coding screens to date. We evaluated the most common design and analysis platforms and observed that functional elements are enriched at DHS/H3K27ac sites, while the strongest sgRNAs target the DHS summit. Additionally, we discovered a subtle, previously undescribed target DNA strand-bias for CRISPRi in transcribed regions. Our dataset represents a broad resource for the community, establishes a standardized data format, provides guidelines for screen implementation, and uncovers fundamental insights into gene regulation.
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 | Yao, David |
|---|---|
| Degree supervisor | Bassik, Michael |
| Thesis advisor | Bassik, Michael |
| Thesis advisor | Engreitz, Jesse |
| Thesis advisor | Greenleaf, William James |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Degree committee member | Engreitz, Jesse |
| Degree committee member | Greenleaf, William James |
| Degree committee member | Kundaje, Anshul, 1980- |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Department of Genetics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | David Yao. |
|---|---|
| Note | Submitted to the Department of Genetics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/vk828vb5651 |
Access conditions
- Copyright
- © 2023 by David Yao
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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