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Principal Investigator: JING ZHANG
Organization: UNIVERSITY OF CALIFORNIA-IRVINE
Fiscal Year: 2024
Award: $394,000
Funding agency: National Human Genome Research Institute
PROJECT SUMMARY
In eukaryotes, transcriptional regulation is essential to maintaining cell identity, responding to
intra- and extra-cellular signals, and coordinating gene activities, whereas its dysregulation can
cause a broad range of disorders. Previous methods mainly used averaged genomic signals from
thousands of cells to investigate gene regulation, failing to reveal the regulatory heterogeneity
across diverse cell states. Recent advances in multimodal single-cell technologies provide new
opportunities to decipher the cell-type-specific regulation code at the finest resolution possible.
However, its computational modeling is still in its infancy due to the high dimensionality,
missingness, vulnerabilities to confounding factors, and complex feature interactions. In this
project, we aim to develop a suite of computational models to construct gene-centric, personal
regulome via single-cell multiome integration and link multi-scale dysregulations to disease.
Distinct from previous efforts reporting a set of one-dimensional (1D) functional cis-regulatory
elements (CREs) from only one genome and applying it to all samples, we aim to construct
personal, compact, gene-centric, and cell-type-specific transcriptional regulome from sc-
multiome data. Specifically, we will first propose a scalable multimodal deep generative model to
integrate single-cell data with single-, multi-, and hybrid modalities. Distinct to existing methods,
we will include an invariant representation learning scheme to derive latent cell representations
uncorrelated with confounding factors (e.g., age, gender, read depth, and batch effects) for bias-
free transcriptome and epigenome reconstruction (Aim 1). Then, we will go beyond the 1D
genome annotation by deciphering multi-scale gene regulation code (Aim 2), including i)
functional CREs at a base-pair resolution; ii) CRE target genes for functional interpretation; iii)
transcription factor regulatory networks. Lastly, we will develop interpretable deep learning
models to link multi-scale dysregulations to disease with mechanistic explanation (Aim 3).
This proposal is built on a close and long-term collaboration between Dr. Jing Zhang, an
expert in computational biology and machine learning at the University of California, Irvine, with
Dr. Feng Yue, an expert in regulatory genomics and 3D genome organization at the Northwestern
University. Upon completion, our proposed methods will substantially deepen our understanding
of transcriptional regulation to a single-cell level resolution and quantitatively relate multi-scale
risk factors to genetic disorders. In addition, our aims will yield open-source software for the
scientific community as essential tools for single-cell multi-omics data processing and integration.
Terms: <21+ years old><3-D><3-Dimensional><3D><Abscission><Adult><Adult Human><Age><Assay><Basal Transcription Factor><Basal transcription factor genes><Base Pairing><Benchmarking><Best Practice Analysis><Bioassay><Biological><Biological Assay><Body Tissues><Boundary Elements><Brain><Brain Nervous System><CRISPR><CRISPR/Cas system><California><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><ChIP Sequencing><ChIP-seq><ChIPseq><Clustered Regularly Interspaced Short Palindromic Repeats><Code><Coding System><Collaborations><Communities><Complex><Computational Biology><Computer Models><Computer software><Computerized Models><Data><Disease><Disorder><Dropout><Encephalon><Engineering><Eukaryota><Eukaryote><Event><Excision><Extirpation><Functional RNA><Gender><Gene Action Regulation><Gene Expression Regulation><Gene Regulation><Gene Regulation Process><Gene Transcription><General Transcription Factor Gene><General Transcription Factors><Generations><Genes><Genetic Diseases><Genetic Transcription><Genetic predisposing factor><Genome><Genomics><Genotype><Heterogeneity><Hi-C><Human><Hybrids><Individual><Intracellular Communication and Signaling><Label><Learning><Link><Machine Learning><Methods><Modality><Modeling><Modern Man><Multiomic Data><Non-Coding><Non-Coding RNA><Non-translated RNA><Noncoding RNA><Nontranslated RNA><Performance><Phenotype><Process><RNA Expression><Regulation><Regulatory Element><Removal><Reporting><Research><Resolution><Risk Factors><Sampling><Scheme><Scoring Method><Sensitivity and Specificity><Signal Transduction><Signal Transduction Systems><Signaling><Single cell seq><Software><Specificity><Surgical Removal><Techniques><Tissues><Transcription><Transcription Factor Proto-Oncogene><Transcription Regulation><Transcription factor genes><Transcriptional Control><Transcriptional Regulation><Universities><Untranslated RNA><Validation><Variant><Variation><adulthood><ages><benchmark><biologic><biological signal transduction><cell type><chromatin immunoprecipitation-sequencing><computational modeling><computational models><computational suite><computer based models><computer biology><computerized data processing><computerized modeling><data integration><data processing><deep learning based model><deep learning model><dimension reduction><dimensionality reduction><epigenome><extracellular><flexibility><flexible><functional genomics><generative models><genetic condition><genetic disorder><genetic risk factor><genome annotation><genome profiling><genomic profiling><global gene expression><global transcription profile><high dimensionality><infancy><infantile><inherited factor><machine based learning><member><multi-modality><multimodality><multiomics><multiple omic data><multiple omics><neural><noncoding><open source><panomics><reconstruction><reduce data dimension><reduce dimensionality><resection><resolutions><sequencing platform><single cell next generation sequencing><single cell sequencing><single cell technology><super high resolution><superresolution><supervised learning><supervised machine learning><three dimensional><tool><transcription factor><transcriptome><ultra high resolution><user friendly computer software><user friendly software><validations>