Our application of high-dimensional immune-phenotyping using CyTOF finds natural applications in clinical settings. There has been an explosion of experimental methodologies for measurement multiplexing in immunology (e.g. cytokine arrays by SomaLogic, single-cell RNAseq) and we are focusing on CyTOF for its close connection to biological function in immunological problem. Our main project has been to develop new machine-learning derived tools to automatically analyze CyTOF data (collaboration with Pankaj Mehta's theoretical physics group at Boston University). This collaboration (funded in part by a seed grant from the Moore foundation) synergizes Pankaj Mehta's expertise in machine learning and the Altan-Bonnet lab's expertise in quantitative immunology to optimize new methods in CyTOF data analysis. We emphasize interpretability of immunological classifier to allow immunologists to leverage the results of our machine learning pipeline into testable hypotheses and experimental validation. We are collaborating with clinical labs at the NIH to apply our experimental/theoretical pipeline to analyze clinical samples from ongoing clinical trials. Our collaboration with Mike Lenardo's group at NIAID led to the design and validation of a CyTOF panel that provides general immunophenotyping for peripheral blood mononuclear cell (PBMC) samples for the clinical genomics group at NIAID. This 35-label panel covers all the main leukocyte cell types of the blood and leaves open channels for augmentation with antibodies against disease-specific epitopes. We are profiling the PBMC of patients afflicted with XMEN (X-linked immunodeficiency with magnesium defect, EBV infection, and neoplasia) and ALPS (Autoimmune lymphoproliferative syndrome) under study within the Lenardo lab. Preliminary measurements demonstrate how the large number of available samples within the clinical genomics branch at NIH can be leveraged with our high-dimensional phenotyping to generate immunological classifier that best correlates with disease status. We will apply support-vector classifiers to identify the leukocyte populations whose variation in frequency and/or change in differentiation status best correlates with clinical scores. Moving forward, we will further this collaboration to build a custom-designed experimental and computational pipeline that robustly classifies patients from multiple primary immunological deficiencies. Hence, our goal is to fine-tune our machine learning tools to clinical applications, while probing the global disruption of immunological homeostasis (as studied in project I) with access to rare samples of patients with primary immunodeficiencies. Similar collaborative work is ongoing with Mariana Kaplan's lab within NIAMS. This collaboration aims at deepening our understanding of the dysregulation of neutrophils in Systemic Lupus Erythematosus (SLE) patients. The Kaplan lab has identified a new population of low-density granulocytes that trigger enhanced formation of neutrophil entrapment traps (NETosis), IFN secretion and vascular damage. Our working hypothesis for this collaboration is that such neutrophilic dysfunction in SLE globally displaces homeostasis by maintaining chronic inflammation. Here we expanded our general immune-phenotyping CyTOF panel with antibodies specific to neutrophils. Analysis of neutrophils requires the processing of fresh whole blood and the Kaplan lab receive fresh samples biweekly from patients at the NIH hospital. For this reason, our CyTOF-based immune-phenotyping pipeline is particularly well suited to identify large-scale disruption of immune homeostasis in the blood of SLE patients, as we have validated the robustness of the pipeline when fresh samples are being accrued, processed and analyzed over a large period of time (1 month). Hence, we will accrue samples (n100) with varied clinical presentations in order to focus our understanding of altered homeostasis in the blood of SLE patients. As in the collaboration with the Lenardo lab, we are taking the opportunity of collaborating with the Kaplan lab to fine-tune our CyTOF pipeline in clinical settings, as well as to further probe how global immunological disruption can be set in the context of chronic inflammation.
