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Robust and scalable Bayesian analysis tools for single cell CRISPR screens with sequencing- and imaging-based readouts

Award Number: F31HG013609
ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

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Project Summary Single-cell CRISPR screens enable genetic perturbation studies of the functional genome at unprecedented scale and resolution. There are two major types of single cell CRISPR screens: sequencing-based screens and imaging-based screens. Sequencing-based screens measure the impact of CRISPR-induced genetic perturbations on phenotypes which can be read out via single cell sequencing, such as transcriptome-wide gene expression or chromatin accessibility. Imaging-based screens profile a range of image-based cellular phenotypes using fluorescence microscopy, including cell morphology and the localization of fluorescently-tagged proteins. Together, these two types of single-cell CRISPR screens can study diverse cellular behaviors which were previously inaccessible to pooled genetic screens. However, the datasets generated by these screens are large and complex, requiring fast and accurate computational analysis tools to interpret the phenotypic effects of each genetic perturbation. Yet existing methods are often not statistically robust or are otherwise prohibitively slow to analyze current datasets of millions of cells across thousands of phenotypic dimensions. This proposal explores the use of Bayesian hierarchical models to unlock robust and scalable analysis of single-cell CRISPR screen data. We will design two tools based on this framework: one for single-cell CRISPR screens with gene expression readouts (Aim 1.1) and one for optical pooled screens (Aim 2.1). Both tools will use state-of-the-art statistical methodologies to quickly and robustly infer which perturbations affect which phenotypes. This task is a critical step towards deepening our understanding of biological pathways and gene regulatory networks using these large-scale perturbation datasets. We will design and benchmark our methods using public datasets from both screen modalities to evaluate their performance and generalizability. To demonstrate our methods’ ability to generate biological insights in clinically relevant contexts, we will apply our tools to new screens from our collaborators to aid in nominating therapeutic targets for blood disorders (Aim 1.2) and neurological diseases (Aim 2.2). The proposed work will be broadly useful to practitioners of single-cell CRISPR screens and will help to democratize these complex screening approaches for widespread use.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $43,203 )
2024	2024	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Human Genome Research	000	1	5/8/2024	NEW	$41,923
2024	2024	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Human Genome Research	001	1	8/22/2024	NEW	$1,280
														Subtotal = $43,203

Grand Total All Awards = $43,203

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Robust and scalable Bayesian analysis tools for single cell CRISPR screens with sequencing- and imaging-based readouts

Award Number: F31HG013609

ORGANIZATION: NATIONAL HUMAN GENOME RESEARCH INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

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