R21HL156234 Integrative Analysis of Bulk and Single-Cell RNA-Seq Data for Cardiometabolic Disease (2021-2023)
PI: Mingyao Li
In the past decade, there has been a dramatic increase in cardiometabolic diseases (CMD) that include obesity, type 2 diabetes, stroke, and atherosclerotic cardiovascular disease. This application seeks to perform novel statistical analysis on publicly available gene expression and genetics data to characterize cell-type composition and cell-type-specific gene expression changes in human adipose, and to elucidate the functional roles of genetic findings on CMD.
R01EY030192-02S1 Administrative supplement on Alzheimer’s disease (2020-2022)
PI: Mingyao Li
The retina is widely considered as a window to the brain and is involved in the development of Alzheimer’s disease (AD). Despite the increasing evidence of retina involvement in AD, the underlying molecular and cellular mechanisms for these changes in retina are still poorly understood. This application will generate data from retina in AD patients to test the hypothesis that measurable molecular deficits occur in the retinas of individuals diagnosed with AD.
R21EY031877 Integrative Analysis of Bulk and Single-Cell RNA-Seq Data from Human Retina for Age-Related Macular Degeneration (2020-2022)
PIs: Mingyao Li and Rui Xiao
Age-related macular generation is the third largest cause of vision loss, affecting over 10 million Americans. This application seeks to perform novel statistical analysis to publicly available gene expression and genetics data to characterize cell type composition and cell type-specific gene expression changes in human retina, and to elucidate the functional roles of genetic findings on age-related macular degeneration.
R01EY031209 Deconstructing and Modeling the Single-Cell Architecture of the Age-Related Macular Degeneration Retina and RPE/Choroid (2020-2024)
PIs: Dwight Stambolian and Mingyao Li
Diseases that affect the retina and supporting structures are complex and it remains challenging to assess if pathological phenotypes originate in diverse cell populations or highly specific cell types. This application will address the urgent need to collect and analyze cells from postmortem human eyes to address the question about which cell types are involved in age-related macular degeneration.
R01HL150359Computational and Functional Strategies to Decipher LncRNAs in Human Atherosclerosis (2020-2024) PIs: Muredach Reilly and Mingyao Li
This proposal addresses knowledge gaps for long non-coding RNAs (lncRNAs) in human atherosclerosis and cardiovascular disease (CVD) risk. We propose innovative integration of large-scale bulk RNAseq coupled to selective single cell (sc)RNAseq profiling of human atherosclerotic plaques and a novel deconvolution algorithm to identify lncRNAs, and their host cell subpopulations, that associate with symptomatic/unstable plaques and coronary heart disease events. These findings, coupled to our proposed cell-specific functional studies, will drive in vivo modeling and precision therapeutic targeting of vascular lncRNAs in atherosclerotic CVD.
R01EY030192 Single-Cell Transcriptomic Analysis of Human Retina (2019-2023)
PI: Mingyao Li
The retina is the most vital for normal perception of an image in the eye. Diseases that affect the retina are complex, and it remains challenging to assess if pathological phenotypes originate in diverse cell populations or highly specific cell types. This application will address the urgent need to collect and analyze retinal cells from postmortem human eyes to advance our understanding of human retinal diseases. This study also includes new computational method development to address analytical challenges for single-cell RNA-seq data analysis.
R01HL113147 Elucidation of Tissue-Specific Transcriptomic Profiles in Cardiometabolic Disease (2018-2023)
PIs: Muredach Reilly and Mingyao Li
Although recent clinical trials and human genetic studies implicate monocytes and macrophages in atherosclerotic cardiovascular disease (CVD), we have yet to optimize monocyte targeted therapeutics for CVD and human cardiometabolic disorders (CMD). In this proposal, we propose cutting-edge single cell RNAseq profiling of human monocytes coupled to innovative population genetics and functional genomics to define the role of human monocyte subpopulations in human monocyte biology and CMD. Our study addresses a major knowledge gap in human monocyte biology by defining the landscape of human monocyte subpopulations, their regulatory features, genetic association with disease, and functional genomics of key monocyte subpopulation regulatory genes in human model systems.
Cells are the basic biological units of multicellular organisms. The collection of abundances of all RNA species in a cell forms its “molecular fingerprint”, enabling the investigation of many fundamental biological questions beyond those possible by traditional bulk RNA sequencing experiments. This proposal addresses critical statistical challenges in single-cell RNA sequencing analysis. The successful completion of this project will allow researchers to better disentangle complex cellular heterogeneity, precisely relate genomic sequence to gene regulation, and facilitate the translation of basic research findings into clinical studies of human disease.
Past PI grants
R01GM108600 Statistical Methods for Transcriptomic Profiling using RNA Sequencing (2014-2020)
Penn IOA Elucidation of Cell-Type Specific Transcriptomic Profiles in Neurodegenerative Brain (2017-2018)
R01HL113147 Elucidation of Tissue-Specific Transcriptomic Profiles in Cardiometabolic Disease (2012-2018)
R01HG005854 Statistical Methods for Gene Mapping Studies in Admixed Populations (2009-2015)
R01HG004517 Development of Statistical Methods for Disease Gene Discovery (2008-2014)