We are an interdisciplinary group interested to advance basic and translational immunology through the generation and interpretation of high-throughput data. We also develop computational tools for statistical analysis of the data types we work with.
Characterizing T cell functional states during the progression of cancer and autoimmunity
T cells play an important role in cancer suppression and in the maintenance of homeostasis (preventing autoimmunity). In uncontrolled disease T cells can progressively lose functionality (e.g. in cancer) or become over-activated (e.g. in autoimmune disease), and changing the T cell state can result in an improved phenotype (e.g. cancer clearance).
In the Singer lab we work to understand how changes in the T cell states over time can affect tumor clearance and the progression of autoimmune disease. To this end we are using single-cell transcriptomics to generate hundreds of thousands of T cell transcriptomic measurements during disease progression. We design tailored computational frameworks to identify and characterize the different T cell functional states present in these diseases, along with their dynamic role during disease progression. We use follow-up functional assays and genetic perturbations (e.g. with CRISPR) to validate our predictions and identify novel targets for therapeutic interventions.
We are currently focusing on several cancer (melanoma, colon) and autoimmune (MS, colitis) models.
Identifying gene interaction networks underlying response to immunotherapies
Immunotherapies are showing unprecedented success at clearing tumors in the clinic and are the focus of many ongoing clinical trials, but many patients still fail to respond. Improving our understanding of the molecular mechanisms underlying immune system rejuvenation following singleton or combination therapy will lead to hypothesis-driven design of therapies and improved prognosis.
The Singer lab works to elucidate regulatory mechanisms that underlie an immune response following different therapeutics and their combinations. Using computational frameworks we compare single-cell transcriptional datasets across different, potentially dependent, conditions (e.g. checkpoint blockades) and annotate altered regulatory pathways for each condition along with regulatory non-linear dependencies in the combinatorial setting. We test our predictions and refine our models using functional assays and in vivo models.
We are currently focusing on mouse melanoma and EAE models as well as patient derived data.
Computational tools for Systems Immunology
These are very exciting times for computational biologists: recently introduced technologies now enable the generation of high-throughput biological data at unprecedented breadth and depth. For example, the transcriptomes of hundreds of thousands of cells can be measured at single-cell resolution in a single afternoon!
Leveraging these exciting technologies to address scientific questions requires the design and implementation of statistically sound frameworks, to account for the biases introduced and to test novel queries enabled by these datasets. In the Singer group we develop computational tools for the analysis and interpretation of high-throughput immunology data. Several examples include the identification of marker combinations for cell subpopulations, T cell lineage inference by single-cell TCR analysis, and the annotation of gene modules while controlling for biological variance and technical noise.