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    Vanderbilt Univesity

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  • Biography

    Irish Lab

    Vanderbilt University

    A central goal of our research at Vanderbilt is to understand how changes at the single cell level alter signaling in healthy cells and lead to therapy resistant populations in human diseases.  In the Irish Lab, we use new tools and computational approaches to do basic and translational research in human cancer and immunology.

    In addition to making discoveries at the frontier of human genetics and immunology, we aspire to use knowledge of cell signaling to create therapeutic technologies and to guide clinical decisions.  In the long term, great potential exists to detect disease earlier and to tailor a patient’s therapy to the biological alterations detected in the cells of their disease.  By better understanding biological systems which control development and cell-cell interactions in healthy and diseased contexts, we can learn to program cells to become therapeutic agents or target malignant signaling events to specifically kill cancer cells.

    Jonathan M. Irish, Ph.D.

    My experimental approach has focused on characterizing cell type and dissecting signaling networks in individual cells from primary tissues, including human tumors.

    Before coming to Vanderbilt in December 2011, I trained at Stanford University with Garry Nolan (as a Ph.D. student) and Ronald Levy (as a Postdoc & Instructor).  There I created a new approach that measures signaling in individual cancer cells and applied it to the study of Acute Myelogenous Leukemia (AML) patient clinical outcomes (Cell 2004).  An advantage of this single cell approach is that signaling can be characterized in rare populations of cancer cells and contrasted with the bulk cancer cell population or with tumor-infiltrating non-malignant cells (Nat Rev Cancer 2006).

    I later applied this technique to healthy B cells (J Immunol 2006) and malignant B cells in follicular lymphoma (FL) (Blood 2006).  In FL, signaling identified a subset of tumor B cells that were present at diagnosis only in patients with a lower overall survival (p < 0.0001) and that increased over time as the patient’s cancer progressed (PNAS 2010).

    Systems biology tools like Cytobank, a cloud computing platform we created to manage and analyze single cell signaling data, were critical to this work (Curr Protoc Cytometry 2010).


    Key technologies in the Irish lab include phospho-specific flow cytometry (phospho-flow) and mass cytometry.  We use these techniques to measure signaling events in individual cells from primary human tissues.  



    Phospho-flow combines lineage and phospho-specific antibodies to measure intracellular signaling in individual cells (Nature Reviews Cancer 2006).  For example, this technique revealed that abnormal B cell receptor (BCR) signaling identifies an aggressive subset of Follicular Lymphoma cells (PNAS 2010).

    Mass Cytometry

    Mass cytometry is a next generation analytical flow cytometry technology capable of measuring 34+ features of individual cells – a dramatic leap forward from the present technology, which routinely measures only 3 to 8 features per cell (Science 2011).  For more information about the innovative aspects of mass cytometry, see Vaporizing Cells in the Name of Science on the Cytobank blog.

    The form of single cell proteomics enabled by mass cytometry provides unique opportunities for mechanistic understanding of signaling in primary tumors and healthy human tissues.

    Detailed protocols are available online here:


    We recently reviewed these technologies:

    Curr Top Microbiol Immunol. 2014;377:1-21. doi: 10.1007/82_2014_367.

    High-dimensional single-cell cancer biology.

    Irish JM1, Doxie DB.

    Computational Biology

    My laboratory is also interested in computational biology, bioinformatics, and modeling and develops tools for our research that:

    1. Identify and compare individual cells in heterogeneous primary tissue samples

    2. Are cloud based and connected to online tools and communities

    3. Capture key experiment annotations and relate them to the raw data files

    4. Integrate pathway modeling and visualization tools into the data analysis workflow