Multi-cellular organisms develop from a single cell that undergoes many stages of proliferation and differentiation that result in the vast array of progenitor and terminal cell types. While dogma defines development as a series of discrete steps, it is actually a continuous process, characterized by transitional stages and intermediate cell types that have yet to be described. We propose reframing development in terms of branching trajectories: We will develop computational algorithms that use high dimensional single cell data to map each individual cell into a lineage tree representing the chronological order of development. Rather than grouping cells into nodes of this tree (cell types), single cells will fill in the branches in a continuum ordered according to their developmental chronology. Thus, each branch is a developmental trajectory that represents all the transitional stages between cell states. In essence, combining single cell technologies with sophisticated computation will empower us to do for Homo sapiens what John Sulston and colleagues did for C. elegans-to construct a complete and detailed map of normal development. This map will provide both a full catalog of all cell types in our body, as well as the developmental relations between them. The detailed resolution of the resulting map will empower developmental studies, help elucidate how and where development is derailed in disease, and guide the next generation of regenerative medicine.

Public Health Relevance

We want to develop methods that will enable the construction of complete atlas that maps out in full detail how our bodies develop from a single embryonic cell. This atlas will become an invaluable reference for the progression of many cancers, as well as developmental disorders such as Rett syndrome, Fragile X syndrome, autism spectrum disorders and autoimmunity. In addition, the tools we will develop and the insights gleaned from these tools could play a crucial role in advanced therapeutic applications of regenerative medicine.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
1DP1HD084071-01
Application #
8755773
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Coulombe, James N
Project Start
2014-09-30
Project End
2019-06-30
Budget Start
2014-09-30
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biology
Type
Graduate Schools
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10027
Azizi, Elham; Carr, Ambrose J; Plitas, George et al. (2018) Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174:1293-1308.e36
DiGiuseppe, Joseph A; Cardinali, Jolene L; Rezuke, William N et al. (2018) PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data. Cytometry B Clin Cytom 94:588-601
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Lavin, Yonit; Kobayashi, Soma; Leader, Andrew et al. (2017) Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 169:750-765.e17
Chevrier, St├ęphane; Levine, Jacob Harrison; Zanotelli, Vito Riccardo Tomaso et al. (2017) An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell 169:736-749.e18
Setty, Manu; Tadmor, Michelle D; Reich-Zeliger, Shlomit et al. (2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol 34:637-45
Prabhakaran, Sandhya; Azizi, Elham; Carr, Ambrose et al. (2016) Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. JMLR Workshop Conf Proc 48:1070-1079
Gut, Gabriele; Tadmor, Michelle D; Pe'er, Dana et al. (2015) Trajectories of cell-cycle progression from fixed cell populations. Nat Methods 12:951-4
DiGiuseppe, Joseph A; Tadmor, Michelle D; Pe'er, Dana (2015) Detection of minimal residual disease in B lymphoblastic leukemia using viSNE. Cytometry B Clin Cytom 88:294-304
Levine, Jacob H; Simonds, Erin F; Bendall, Sean C et al. (2015) Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell 162:184-97

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