Branching developmental pathways through high dimensional single cell analysis in trajectory space: application to the tumor environment and tumor control of the immune response

Identification: Dermadi, Denis


Description

Branching developmental pathways through high dimensional single cell analysis in trajectory space: application to the tumor environment and tumor control of the immune response

Denis Dermadi1*, Nicole Lazarus1, Michael Bscheider1, Nicole Salazar1, Husein Hadeiba2, Eugene Butcher1
1Laboratory of Immunology and Vascular Biology, Department of Pathology, Stanford University School of Medicine; 2Palo Alto Veterans Affairs Institute for Research
*Corresponding author

Cancer immunotherapy has proven successful with new generations of drugs (e.g. checkpoint inhibitors) that target lymphocytes and their surface molecules, however only small fraction of the patients benefits from these drugs. Thus, it is important to uncover additional mechanisms of tumor immune evasion. Lymphocytes are recruited into tumor by endothelial cells (ECs), which are the interface between the tumor and circulating blood. Tumors induce specialized ECs, known as high endothelial venules (HEV). Induction of HEVs in tumors correlates with favorable prognosis, but many tumors use HEVs to recruit immune suppressive cells. Tumor microenvironment through conditioning of the local endothelium trafficking programs mediates recruitment of immune suppressive lymphocytes and enhances carcinogenesis. Interaction of tumors may also alter the imprinting of lymphocyte adhesion/trafficking programs in tumor draining lymph nodes (LN) either through effects on tumor derived dendritic cells or directly when the LN is invaded by metastatic cells.

We take advantage of mass cytometry (CyTOF) and single cell RNA-Seq to monitor the induction and regulation of EC and leukocyte trafficking receptors in B16, B16 OVA (more immunogenic), 4T1 and colon mouse tumors. High dimensional data, rich in information, is analyzed using an innovative single cell analysis algorithm, which aligns cells in developmental trajectories using protein or gene expression(s) of single cells. This algorithm has the potential to reveal, for the first time in an unsupervised manner, complex branching and allows the detection of new functionally important cells or specific transient cell populations.

Credits

Credits: None available.

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