Characterization of Complex Immune Cell Phenotypes and Spatial Interactions in the Tumor Immune Microenvironment Using Imaging Mass Cytometry
The Merchant Laboratory investigates the mechanism for disease progression and treatment resistance in diffuse large B-cell lymphomas, Hodgkin's lymphomas, multiple myeloma and myelofibrosis by characterizing the composition and complexity as well as spatial interactions among tumor and immune cells in the tumor microenvironment using imaging mass cytometry (IMC), a multiplex imaging platform that allows concurrent detection of 40+ markers and enables high-dimensional, single-cell analysis with spatial information of the different cell types at sub-cellular resolution.
The advancement of mass cytometry using Fluidigm's Helios (CyTOF) platform enables the detection of a large number of surface markers, transcription factors and intracellular cytokines without the need for compensation due to spectral overlap as with traditional flow cytometry. In the Merchant Lab, immune system monitoring of leukemia and lymphoma patients enrolled in clinical trials is performed using mass cytometry and a variety of other techniques. We are able to characterize and track the makeup and functionality of the patients' immune systems, with resolution down to a single cell level, before, during and after treatment/intervention. This information provides critical insights and may enable us to pinpoint key factors differentiating responders and non-responders.
This platform is rapidly expanding—increasing the number of possible targets—and can be applied to many other studies involving the identification, quantification or characterization of a heterogenous cell population. Additional complementary experiments including single-cell RNA sequencing, proteomics/metabolomics profiling as well as in vitro cytotoxic killing assays and culturing of tumors in the presence of small molecule immune checkpoint inhibitors give a more complete picture and a deeper understanding of what is taking place inside the body. The Merchant Lab hopes to use the results to identify patients who have biomarkers of poor prognosis that may require alternative or additional interventions or monitoring and ultimately enable doctors to provide less toxic, more targeted treatment strategies.
Statistical Learning Algorithm Development in the Context of Lymphomas
Currently, the Merchant Lab is constructing machine-learning methodologies that learn from both single-cell sequencing technology and in situ spatial interactions designed to inform and challenge the current understanding of lymphoma cancers.
Our aim is to generalize spatial genomic models and identify pan-cancer and disease specific features that motivate targetable intervention therapy.