Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program
arXiv:1903.07231 · doi:10.1038/s41586-019-1629-x
Abstract
Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.
20 pages, 3 figures
References in corpus (1)
Cited by in corpus (13)
- Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program
- WILDS: A Benchmark of in-the-Wild Distribution Shifts
- Cell types and ontologies of the Human Cell Atlas
- Guidelines for reporting single-cell RNA-Seq experiments
- Key challenges facing data-driven multicellular systems biology
- 3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison
- A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
- Optimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy Framework
- Construction and Usage of a Human Body Common Coordinate Framework Comprising Clinical, Semantic, and Spatial Ontologies
- Ensuring Adherence to Standards in Experiment-Related Metadata Entered Via Spreadsheets
- Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
- IBEX: An open and extensible method for high content multiplex imaging of diverse tissues
- Pathological Primitive Segmentation Based on Visual Foundation Model with Zero-Shot Mask Generation