Multi-omic 3D tissue maps for a Human BioMolecular Atlas
EMSL Project ID
60201
Abstract
Spatially resolved molecular maps of mammalian organs hold significant promise in providing a deeper under-standing of human organ functioning in health and disease states. Fundamental to this is an understanding how tissue organization impacts on the state of a cell and performance of its function. The overarching goal of the Human BioMolecular Atlas Program (HuBMAP) and specifically of Tissue Mapping Centers within the HuB-MAP framework is to generate high-resolution three dimensional (3D) human tissue maps of non-diseased hu-man organs. In this project, we aim to utilize a mass spectrometry (MS)-based omic mapping technology to perform unbiased detection and mapping proteins of the pancreas. Our focus will be on the pancreas, an es-sential organ important for several metabolic functions. We will employ high resolving power and high-resolution mass spectrometry-based molecular mapping platforms (LMD-nanoPOTS-MS) for unbiased mapping of pro-teins. The mass spectrometry assay will be complemented with powerful highly multiplexed targeted sub-cellular resolution spatial omics assays (NanoString GeoMx for protein and RNA respectively, performed by our collaborators at University of Florida), metabolite mapping (in collaboration with Vanderbilt University) and multiplexed immunofluorescence microscopy to generate high-resolution, multi-omics human tissue maps (in collaboration with the Texas Advanced Computing Center). The innovative spatially resolved multi-omic tissue maps generated will be unprecedented and the unique multi-omic datasets will provide many novel insights. The tissue mapping efforts will be supported by commercially available and open-source state-of-the-art 3D recon-struction software to create browsable 3D RNA/protein/metabolite map of the pancreas.
Project Details
Start Date
2021-08-31
End Date
2023-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members
Related Publications
Yumi Kwon, Paul D. Piehowski, Rui Zhao, Ryan L. Sontag, Ronald J. Moore, Kristin E. Burnum-Johnson, Richard D. Smith, Wei-Jun Qian, Ryan T. Kelly, Ying Zhu. 2022. "Hanging drop sample preparation improves sensitivity of spatial proteomics." Lab on a Chip 22 (15):2869-2877. https://doi.org/10.1039/d2lc00384h