Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape

推断单细胞 DNA 甲基化及其空间景观的计算方法

• 批准号: 10665219
• 负责人: Wenpin Hou
• 金额: $ 24.9万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665219
• 关键词: 3-Dimensional; Accounting; Address; Aging; Atlases; Award; Biometry; Cells; Chromatin; Clinical Trials; Code; Communities; Computing Methodologies; CpG dinucleotide; DNA; DNA Methylation; DNA Methyltransferase Inhibitor; DNA Sequence; Data; Data Set; Development; Developmental Process; Disease; Doctor of Philosophy; Early Diagnosis; Emerging Technologies; Encyclopedia of DNA Elements; Engineering; Epigenetic Process; Evaluation; Funding; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genomic DNA; Genomic Segment; Genomics; Goals; Hong Kong; Human; Immune checkpoint inhibitor; Immune response; Joints; Lead; Link; Location; Malignant Neoplasms; Mammals; Mathematics; Measurement; Measures; Medicine; Mentors; Methodology; Methods; Modeling; Multiomic Data; Mutation; Outcome; Patients; Pharmaceutical Preparations; Phase; Phenotype; Population; Population Heterogeneity; Postdoctoral Fellow; Prediction of Response to Therapy; Prognosis; Promoter Regions; Proteins; Protocols documentation; Public Health Schools; Regulation; Regulator Genes; Research; Research Personnel; Resolution; Resources; Sample Size; Sampling; Series; Signal Transduction; Site; Statistical Methods; System; Techniques; Technology; Therapeutic; Tissues; Training; United States National Institutes of Health; Universities; Ursidae Family; Variant; Work; career; career development; cell type; clinical practice; clinical translation; computing resources; deep learning; deep learning model; developmental disease; disease classification; disorder prevention; epigenome; epigenomics; experimental study; gene regulatory network; genomic data; improved; insight; interest; multimodal data; novel; programs; reference genome; single cell analysis; single-cell RNA sequencing; statistics; targeted treatment; therapeutic target; tool; transcriptome sequencing; transcriptomics

Project Summary/Abstract The candidate, Wenpin Hou, PhD, is an applied mathematician and computational biologist serving as a postdoctoral fellow of Biostatistics at Johns Hopkins Bloomberg School of Public Health (JHSPH). Her long-term career goal is to improve clinical practice in therapy for diseases by developing computational and statistical methods to decipher spatial and temporal gene regulatory programs using multi-omics data and implementing these methods into developmental process and diseases. The research she proposes entitled Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape combines advanced spatial transcriptomics techniques with computational methods to infer spatial DNA methylation landscape, which enables the accurate evaluation of the epigenomic spatial variability and epigenomic targets in therapy for diseases. Dr. Hou completed her PhD in Mathematics at The University of Hong Kong where she focused on inferring and controlling gene regulatory networks (GRNs). Her first postdoctoral research (2017-2019) with Drs. Aravinda Chakravarti and Suchi Saria at Johns Hopkins University (JHU) represented the first shift in focus of GRNs from theoretical to computational genomics. Her second postdoctoral research (2019-present) with Drs. Hongkai Ji and Stephanie Hicks at JHSPH has further provided complementary training in reconstructing and predicting spatial transcriptomic and epigenomic landscape using single-cell data. Dr. Hou's mentoring team consists of Hongkai Ji (primary mentor), PhD, an expert in developing computational and statistical tools for analyzing single-cell genomic data, including reconstructing and predicting temporal and spatial transcriptomic and epigenomic landscape; Stephanie Hicks (co-mentor), PhD, an expert in developing statistical methods to address technical variability and spatial transcriptomics in single cells; and Andrew Feinberg (co-mentor), PhD, one of the founders of the field of cancer epigenetics who directs the first NIH funded Epigenome Center. This offers the opportunity to tackle significant challenges in the intersection of statistics, epigenomics and spatial transcriptomics with advanced experimental techniques. Her scientific advisors are Drs. Kasper Hansen, Gregory Hager and Xiaobin Wang who have leading expertise in computational epigenomics, deep learning, clinical translation and disease prevention, respectively. Leveraging the intellectual, experimental, and computing resources from all mentors and advisors as well as through JHSPH, JHU and Johns Hopkins Medicine, Dr. Hou will receive intensive training, mentoring and career development to achieve the goals proposed in this application and have productive outcomes during the award period. Aim 1 will develop methods to predict DNA methylation landscape using bulk gene expression. Aim 2 will develop methods to predict single-cell DNA methylation and differential DNA methylation. Aim 3 will reconstruct tissue-spatial DNA methylation landscape at the single-cell level, generate evaluation datasets in spatial context, and perform across-study assessments using data from The Encyclopedia of DNA Elements (ENCODE), Human Cell Atlas, and Recount2.

项目摘要/摘要 候选人Wenpin Hou博士是一位应用数学家和计算生物学家 约翰·霍普金斯彭博公共卫生学院（JHSPH）生物统计学的博士后研究员。她的长期 职业目标是通过开发计算和统计来改善疾病治疗的临床实践 使用多论数据并实施的方法来解密空间和临时基因调节程序 这些方法进入发育过程和疾病。她提出的题为《计算》的研究 推断单细胞DNA甲基化及其空间景观的方法结合了晚期空间 具有计算方法的转录组学技术来推断空间DNA甲基化景观，哪些 可以准确评估表观基因质空间变异性和表观基因组靶标的治疗 疾病。 Hou博士在香港大学完成了数学博士学位 推断和控制基因调节网络（GRNS）。她的第一次博士后研究（2017-2019）与Drs。 约翰·霍普金斯大学（JHU）的Aravinda Chakravarti和Susti Saria代表了重点的首次转变 从理论到计算基因组学的GRN。她的第二次博士后研究（2019年至今）与Drs。 JHSPH的Hongkai Ji和Stephanie Hicks进一步提供了重建和 使用单细胞数据预测空间转录组和表观基因组景观。侯博士的心理团队 由Hongkai JI（主要导师），博士学位，开发用于计算和统计工具的专家 分析单细胞基因组数据，包括重建和预测临时和空间转录组 和表观基因组景观； Stephanie Hicks（Co-Encertor），博士，开发统计方法的专家 解决单细胞中的技术变异性和空间转录组学；和安德鲁·费恩伯格（Andrew Feinberg）（同事），博士 指导第一个由NIH资助的表观基因组中心的癌症表观遗传学领域的创始人之一。这 提供了在统计，表观基因组学和空间的交集中解决重大挑战的机会 带有高级实验技术的转录组学。她的科学顾问是Drs。卡斯珀·汉森（Kasper Hansen）， Gregory Hager和Siaobin Wang在计算表观基因组学，深度学习， 临床翻译和疾病预防。利用智力，实验和计算 来自所有导师和顾问的资源以及JHSPH，JHU和JOHNS HOPKINS MEDICENT，HOU博士 将接受密集的培训，心理和职业发展，以实现此处提出的目标 申请并在奖励期内有产品结果。 AIM 1将开发预测DNA的方法 使用大量基因表达的甲基化景观。 AIM 2将开发预测单细胞DNA的方法 甲基化和差异DNA甲基化。 AIM 3将重建组织空间DNA甲基化景观 单细胞级别，在空间上下文中生成评估数据集并执行跨研究评估 使用来自DNA元素百科全书（编码），人类细胞图集和recount2的数据。