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The Jackson Laboratory Senescence Tissue Mapping Center (JAX-Sen TMC) - Data Analysis Core

杰克逊实验室衰老组织绘图中心 (JAX-Sen TMC) - 数据分析核心

基本信息

批准号：
10683393
负责人：
Sheng Li
金额：
$ 104.73万
依托单位：
JACKSON LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10683393
关键词：
Adipose tissue Age Aging Antibodies Atlases Benchmarking Biologic Characteristic Biological Biological Markers Cell Aging Cell Communication Cells Classification Collaborations Common Data Element Communities Computational Biology Computer Vision Systems Data Data Analyses Data Analytics Data Coordinating Center Data Set Development Generations Genetic Goals Health Heart Human Hypothalamic structure Image Inbreeding Individual Kidney Linear Models Maps Mass Spectrum Analysis Metadata Modality Molecular Mouse Strains Mus Nuclear RNA Pancreas Pathway Analysis Placenta Policies Population Preparation Procedures Process Proteomics RLK5-associated protein phosphatase Reproducibility Resolution Sampling Standardization Testing The Jackson Laboratory Tissues Visualization aged analysis pipeline cell type cohort computerized data processing data integration data management data sharing networks deep learning design imaging Segmentation mass spectrometric imaging novel marker open source process optimization prospective response senescence single nucleus RNA-sequencing single-cell RNA sequencing synergism tissue mapping tool transcriptome transcriptome sequencing transcriptomics

项目摘要

PROJECT SUMMARY/ABSTRACT DATA ANALYSIS CORE The objective of the Data Analysis Core (DAC) of the JAX-Sen mouse Tissue Mapping Center (TMC) is to construct biomarker datasets and molecular maps of cellular senescence in mice and deliver these data to the SenNet Consortium Organization and Data Coordinating Center (CODCC). The Biological Analysis Core (BAC) of JAX-Sen will generate high-resolution, high-content molecular data to characterize cellular senescence in mouse kidney, adipose tissue, pancreas, placenta, heart and hypothalamus in aged, inbred and genetically diverse mouse cohorts. From these data, the DAC will establish state-of-the-art workflows for data annotation, curation, and analysis of transcriptomic, imaging, and imaging mass spectrometry (IMS) data to build cellular senescence maps. The DAC will collaborate closely with the DAC of the human SenNet TMC at JAX and UConn Health (KAPP-Sen TMC), other human and mouse TMCs and the CODCC to develop and implement Network- wide standards for processing, analyzing and visualizing SenNet data. The JAX-Sen DAC will leverage significant recent advances in deep learning and computer vision to define robust biomarkers of senescent cells and map their tissue context. To achieve its goals, the JAX-Sen DAC will implement rigorous and reproducible data annotation, curation, processing, and dissemination (Aim 1). The DAC will receive and centralize sequencing, imaging, and IMS data from the JAX-Sen BAC. It will automate and harden QC procedures to screen for annotation and curation errors, batch effects, and low-quality samples in collaboration with the BAC. The DAC will implement the pipelines based on common community standards, benchmark on JAX-Sen and reference datasets, and then containerize for sharing with the CODCC. From these data, the JAX-Sen DAC will construct biomarker datasets and molecular maps of cellular senescence (Aim 2). The DAC will deploy analysis pipelines for sequencing, image and IMS data with known and prospective senescence markers to identify and functionally annotate novel biomarkers of senescent cells and map their tissue context, compute the diversity within senescent cell populations, and quantify cell-to-cell interactions between senescent cells and neighboring cells. The DAC will aggregate and integrate these data and metadata to build high-quality maps of cellular senescence. Finally, the JAX-Sen DAC will disseminate JAX-Sen data with rigorous and reproducible open- source processing pipelines (Aim 3). The DAC will harmonize, standardize, and disseminate data processing and analytic pipelines with the CODCC, adhering to common community standards, standard operating procedures, and the network data sharing policy to exact reproducibility. We will collaborate with KAPP-Sen and other CODCC projects and TMCs to identify synergy, e.g., align human and mouse biomarkers on the same tissues.

项目摘要/抽象数据分析核心