Technology

Key technologies in the Irish lab include phospho-specific flow cytometry (phospho-flow), mass cytometry, and machine learning computational biology tools, like t-SNE and MEM.  We use these techniques to measure signaling events in individual cells from primary human tissues.

Phospho-flow

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).

We published a protocol for mass cytometry phospho-flow in Leelatian et al., Methods in Molecular Biology 2015.

Leelatian et al., Methods Molecular Biology 2015

Leelatian et al., Methods Molecular Biology 2015

We also reviewed phospho-flow in cancer research in Doxie & Irish, Current Topics in Microbiology & Immunology 2014.

Doxie & Irish, Current Topics in Microbiology and Immunology 2014

Doxie & Irish, Current Topics in Microbiology and Immunology 2014

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).

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.

We published a protocol for preparing human blood cells for mass cytometry and phospho-flow in Leelatian et al., Methods in Molecular Biology 2015.   We also published a protocol for isolating single cells from human solid tumors and tissue for mass cytometry in Leelatian et al., Current Protocols in Molecular Biology 2017.

Leelatian et al., Current Protocols in Molecular Biology 2017 Fig 1

Leelatian et al., Current Protocols in Molecular Biology 2017 Fig 1

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

For examples, see Diggins et al., Methods 2015 (details) and Diggins et al., Nature Methods 2017 (details).

Diggins et al., Methods 2015 graphical abstract

Diggins et al., Methods 2015 graphical abstract

Diggins et al - Nature Methods 2017 MEM Fig 1

Diggins et al – Nature Methods 2017 MEM Fig 1