TEXAS BIOMED IN-TRAC Research Imaging Core ABSTRACT The Research Imaging Core will leverage key strengths of Texas Biomed, namely a strong cohort of NIH- funded TB investigators with a history of collaboration and with experience in different imaging platforms. IN- TRAC participants will benefit from access to unique NHP resources in the Southwest National Primate Research Center (SNPRC) and its cutting edge imaging capabilities. The Research Imaging Core will also establish an education curriculum to introduce IN-TRAC participants to a variety of cell-based and whole animal imaging platforms. AIM 1: Provide didactic and hands-on training program in confocal microscopy and optical imaging. IN-TRAC participants will learn multi-label immunofluorescence or fluorescence in-situ hybridization (FISH) confocal microscopy, which allows for 3- or 4-D evaluation of tissues and cells with multiple fluorochrome labels. AIM 2. Provide didactic and hands-on training in cutting-edge cellular immunology techniques requiring imaging support or computational analysis. The Texas Biomed Biology, Microscopy and Molecular Cores provide high-dimensional, multi-parameter flow cytometry and analysis as well as BSL3 sorting capabilities. Established TB researchers will provide training live cell imaging (Cytation 5) and scRNAseq (10x Genomics) in BSL3. AIM 3: Provide didactic and hands-on training in live imaging using PET/CT and IVIS systems. Training in whole-body live Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) imaging of NHP and in-vivo imaging system (IVIS) of rodents within the BSL3. IN-TRAC participants will extern at the NHP MRI facilities at UTHSCA to learn the advantages and limitations of this imaging platform. At the completion of the Research Imaging Core, participants will be able to effectively interpret published data that use imaging modalities for NHP, rodents, tissues, cells and RNA; design experiments to test their own hypothesis; and participate in TB research that uses the appropriate animal model.