Single cell mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on cell populations at single-cell resolution. Datasets are generated with antibody panels (upwards of 40) in which each antibody is conjugated to a polymer chelated with a stable metal isotope, usually in the Lanthanide series of the Periodic Table. The antibodies recognize surface markers that delineate cell types and intracellular signaling molecules demarcating multiple cell functions such as apoptosis, DNA damage and cell cycle. By measuring all these parameters simultaneously, the signaling state of an individual cell can be measured at the network level. Given the capabilities of mass cytometry, and recognizing a growing international biomedical and pharmaceutical interest in its application to immunology, diagnostics, and drug development, this Project will extend the current features of mass cytometry to nearly double the number of assayable channels through the creation of novel chelator-isotope pairings as well as new nanodots for highly sensitive detection of surface molecules. Further, we will enable additional virtual channels that increase the number of parameters measured per cell to as many as 200 using advanced signal processing tools such as compressed sensing along with signature based labeling. Finally, we will adapt DNA based amplification techniques to allow for low expressed protein epitope events and RNA copy number measurements down to as few as 5 target antigens measured quantitatively per cell. As per prior years with our other mass Cytometry protocols and computational abilities, developing and perfecting these additional capabilities will greatly enable the other Projects within our U19 center and will serve as a basis for extending these capabilities to others in the biomedical community, including other U19 Centers.

Public Health Relevance

Fluorescence-based flow cytometry has proven an invaluable technology for immunologists and clinicians. It provides critical biological information at the single-cell level regarding immunophenotype, frequency of cell subsets, expression levels of proteins, as well as functional characterization. In our further development of CyTOF as an advanced cytometry tool, we are greatly increasing the utility of the device by developing important new probes and providing them to the research community at large.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI057229-15
Application #
9648673
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Ramachandra, Lakshmi
Project Start
Project End
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
15
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Ju, Chia-Hsin; Blum, Lisa K; Kongpachith, Sarah et al. (2018) Plasmablast antibody repertoires in elderly influenza vaccine responders exhibit restricted diversity but increased breadth of binding across influenza strains. Clin Immunol 193:70-79
Sweeney, Timothy E; Perumal, Thanneer M; Henao, Ricardo et al. (2018) A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun 9:694
Davis, Mark M; Tato, Cristina M (2018) Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? Seeing the Forest Rather than a Few Trees. Cold Spring Harb Perspect Biol 10:
Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16
Keeffe, Jennifer R; Van Rompay, Koen K A; Olsen, Priscilla C et al. (2018) A Combination of Two Human Monoclonal Antibodies Prevents Zika Virus Escape Mutations in Non-human Primates. Cell Rep 25:1385-1394.e7
Wagar, Lisa E; DiFazio, Robert M; Davis, Mark M (2018) Advanced model systems and tools for basic and translational human immunology. Genome Med 10:73
Good, Zinaida; Sarno, Jolanda; Jager, Astraea et al. (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474-483
Satpathy, Ansuman T; Saligrama, Naresha; Buenrostro, Jason D et al. (2018) Transcript-indexed ATAC-seq for precision immune profiling. Nat Med 24:580-590
Vallania, Francesco; Tam, Andrew; Lofgren, Shane et al. (2018) Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Nat Commun 9:4735
Bongen, Erika; Vallania, Francesco; Utz, Paul J et al. (2018) KLRD1-expressing natural killer cells predict influenza susceptibility. Genome Med 10:45

Showing the most recent 10 out of 249 publications