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Principal Investigator: YE ZHENG
Organization: FRED HUTCHINSON CANCER CENTER
Fiscal Year: 2024
Award: $12,355
Funding agency: National Human Genome Research Institute
PROJECT SUMMARY/ABSTRACT
Advances in single-cell technologies have enabled three-dimensional (3D) genome structure profiling and
simultaneous capture of the transcriptome and epigenome within a cell. Quantitative tools are, however, still
unable to fully leverage the unprecedented resolution of single-cell high-throughput chromatin conformation
(scHi-C) data and integrate it with other single-cell modalities. To address this challenge, I propose to (1) Develop
a single-cell gene-body associating domain (scGAD) scoring system to explore single-cell 3D genomics data in
units of genes. (2) Construct machine learning-based models to impute histone modification and 3D chromatin
interaction for simultaneously profiling of each cell's epigenomic features and 3D chromatin architectures.
Subsequently, I will develop an epigenomic regulatory score (ERS) model to infer the cell-type-specific promoter-
enhancer regulation programs at the highest single-cell and single-gene resolution. (3) Validate and extend
scGAD and ERS pipeline to CAR-T immunotherapy study to gain insights into the impact of distal gene regulation
variations on patient responses. In Aim 1, preliminary analysis on human and mouse brain tissues demonstrated
that scGAD extracts gene features agreeing well with the scRNA-seq data from the same system. As a result,
scGAD facilitates the projection of cells from 3D genomics data onto reference panels constructed by scRNA-
seq embeddings with known cell-type annotations. Hence, scGAD provides an unprecedentedly accessible and
accurate cell type annotation method based on 3D chromatin architectures. Furthermore, the successful
integration of cells from different modalities into the same network facilitates information sharing across 3D
chromatin structures, the transcriptome, and the epigenome. Aim 2 leverages such multi-modal networks to build
an ERS model. ERS jointly models the histone profiles at the promoter and distal neighborhoods of the target
gene and the 3D spatial proximity between them. Therefore, the ERS scores quantify the regulatory effects of
distal elements on a per gene and cell basis. Aim 3 will extend the integration framework in Aim 1 and 2 using
scRNA-seq as a multi-modality bridge to CITE-seq data for a deeper annotation, especially for the Peripheral
Blood Mononuclear Cells. This enables the in-depth investigation of the apheresis samples from the Acute
Lymphoma Leukemia patients to gain insight into the roles of distal regulatory elements on gene expression and
their impact on the CAR-T cell therapy responses. To succeed in achieving these aims, I will pursue additional
training with mentor Dr. Steven Henikoff (epigenomics and gene regulation), co-mentors Dr. Raphael Gottardo
(statistics), Dr. Manu Setty (machine learning), Dr. Evan Newell (immunology), and collaborator Dr. Cameron
Turtle (CAR-T cell therapy). Fred Hutchinson Cancer Research Center is an ideal institute for multi-omics single-
cell study with application to immunotherapy, providing cutting-edge research facilities and opportunities for
further career development in a rich interdisciplinary environment. A K99/R00 award will be instrumental in
addressing these challenges and furnishing me with high-level training to launch my independent scientific career.
Terms: <3-D><3-D analysis><3-D modeling><3-Dimensional><3-dimensional analysis><3D><3D analysis><3D modeling><Acceleration><Acute><Acute Lymphoblastic Leukemia><Acute Lymphocytic Leukemia><Acute Lymphoid Leukemia><Address><Agreement><Apheresis><Architecture><Assay><Award><Benchmarking><Best Practice Analysis><Bioassay><Biological><Biological Assay><Blood Component Removal><Body Tissues><Business-Friendly Atmosphere><CAR T cell therapy><CAR T cells><CAR T therapy><CAR modified T cells><CAR-T><CAR-Ts><CIS Gene><CIS protein><CIS-1 Gene><CIS-1 Protein><CISH Protein><CISH gene><CITE sequencing><CITE-seq><CITEseq><Cell Body><Cell Communication and Signaling><Cell Line><Cell Signaling><CellLine><Cells><Cellular Indexing of Transcriptomes and Epitopes by Sequencing><Chromatin><Chromatin Structure><Computational toolkit><Computer software><Cytokine Inducible SH2-Containing Protein><Cytokine-Inducible Inhibitor of Signaling Type 1B><Cytokine-Inducible Inhibitor of Signaling Type 1B Gene><Cytokine-Inducible SH2-Containing Protein Gene><DNA><Data><Deoxyribonucleic Acid><Development><Distal><Educational Achievement><Educational Status><Elements><Engineering / Architecture><Enhancers><Foundations><Fred Hutchinson Cancer Research Center><Frequencies><Functional RNA><G18><G18 Gene><Gene Action Regulation><Gene Expression><Gene Expression Regulation><Gene Regulation><Gene Regulation Process><Genes><Genome><Genomics><Goals><Hemapheresis><Hi-C><Histones><Human><Immune mediated therapy><Immunologically Directed Therapy><Immunology><Immunotherapeutic agent><Immunotherapy><Individual><Intracellular Communication and Signaling><Investigation><Joints><Machine Learning><Maps><Membrane Protein Gene><Membrane Proteins><Membrane-Associated Proteins><Mentors><Methods><Mice><Mice Mammals><Modality><Modeling><Modern Man><Molecular Configuration><Molecular Conformation><Molecular Stereochemistry><Multiomic Data><Murine><Mus><Neighborhoods><Non-Coding><Non-Coding RNA><Non-translated RNA><Noncoding RNA><Nontranslated RNA><Organ><PBMC><Patients><Peripheral Blood Mononuclear Cell><Pheresis><Polycomb><Precursor Cell Lymphoblastic Leukemia><Precursor Lymphoblastic Leukemia><Probabilistic Models><Probability Models><Regulation><Regulatory Element><Repression><Resolution><Role><SOCS Gene><Sampling><Signal Transduction><Signal Transduction Systems><Signaling><Signaling Factor Proto-Oncogene><Signaling Pathway Gene><Signaling Protein><Software><Specific qualifier value><Specified><Statistical Models><Strains Cell Lines><Structure><Suppressor of Cytokine Signaling><Suppressor of Cytokine Signaling Gene><Surface Proteins><System><T cells for CAR><Technology><Three-dimensional analysis><Tissues><Training><Turtles><Untranslated RNA><Variant><Variation><acute lymphatic leukemia><acute lymphogenous leukemia><acute lymphomatic leukemia><benchmark><biologic><biological signal transduction><brain cell><brain tissue><business-friendly environment><career><career development><cell type><cellular indexing of transcriptomes and epitopes by single cell sequencing><chimeric antigen T cell receptor><chimeric antigen receptor (CAR) T cell therapy><chimeric antigen receptor (CAR) T cells><chimeric antigen receptor T cell therapy><chimeric antigen receptor T cells><chimeric antigen receptor T therapy><chimeric antigen receptor fusion protein T-cells><chimeric antigen receptor modified T cells><collaborative atmosphere><collaborative environment><computational framework><computational toolbox><computational tools><computational toolset><computer framework><computerized tools><conformation><conformational><conformational state><conformationally><conformations><cultured cell line><developmental><educational level><epigenome><epigenome profiling><epigenomic profiling><epigenomics><gene regulatory network><genome scale><genome-wide><genomewide><genomic data><genomic data-set><genomic dataset><global gene expression><global transcription profile><histone modification><immune 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RNAseq><single cell expression profiling><single cell technology><single cell transcriptomic profiling><single-cell RNA sequencing><social role><statistical and machine learning><statistical learning><statistical linear mixed models><statistical linear models><statistics><therapeutic response><therapy response><three dimensional><three-dimensional modeling><tool><tortoise><training achievement><training level><training status><transcriptome><transcriptome profiling><transcriptomic profiling><transcriptomics><treatment response><treatment responsiveness><unsupervised clustering>