MAR 21, 2018 08:00 AM PDT

Powering multi-omic translational studies with mass cytometry: characterizing the molecular pacemakers of pregnancy

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  • Assistant Professor, Stanford University School of Medicine, Department of Anesthesiology
      Born in France, Dr. Gaudilliere studied engineering at Ecole Polytechnique before completing an MD-PhD degree from the Harvard-MIT Health Sciences and Technology program. During his postdoctoral fellowship in Garry Nolan's laboratory (Stanford University), Dr. Gaudilliere developed and standardized a pipeline to implement high-dimensional mass cytometry (cytometry by time-of-flight mass spectrometry, the basis of the CyTOF® system) in clinical studies.

      Dr. Gaudilliere is now an assistant professor at Stanford University. His research group combines high-parameter mass cytometry with other proteomics approaches and advanced biocomputational methods to study how the human immune system responds and adapts to acute physiological perturbations. Ongoing studies in the Gaudilliere lab focus on several clinical scenarios including pregnancy and preterm birth (in collaboration with the Bill & Melinda Gates and the March of Dimes Foundations), surgical recovery and traumatic injury and neurocognitive recovery after stroke.

      Dr. Gaudilliere is also a board-certified anesthesiologist and spends 25% of his time working clinically in the operating room.
    • Assistant Professor , Stanford University School of Medicine
        As a student in computer science at the University of Tehran, Nima Aghaeepour, among other activities, helped create a soccer team of robots. After that team ranked first in the 2006 Robocup Worldcup league, he decided that live cells were a more interesting challenge than robots and he joined the life sciences. His graduate research at Stanford University focused on bioinformatics analysis of single-cell data. Together with Mario Roederer at the NIH and Pratip Chattopadhyay, now at New York University Langone Medical Center, he established the very first pipeline that could identify cellular correlates of clinical outcomes from high-dimensional flow cytometry datasets. In addition, the three researchers established the first objective benchmark for evaluation of algorithms that could automatically identify cell types (and, eventually, correlate them with clinical outcomes), namely the Flow Cytometry: Critical Assessment of Population Identification Methods (Flow-CAP). As a postdoc with Garry Nolan (Stanford), and now an independent faculty member, he has been interested in the intersection of data sciences, immunology and clinical phenotyping.
        An alumnus of the Stanford Graduate School of Business Ignite program, Nima also regularly consults for a wide range of companies on both technology development and data analysis. He encourages (and funds) his trainees to take advantage of Stanford's unique entrepreneurship training programs. Nima strongly believes that the next generation of successful academic life scientists will be both multidisciplinary and entrepreneurial.


      DATE: March 21, 2018
      TIME: 8:00AM PST, 11:00AM EST

      Mass cytometry, or cytometry by time-of-flight (the basis of the CyTOF® system), enables quantification of the abundance and functional responses of billions of cells from all major immune subsets simultaneously. In this webinar, Brice Gaudilliere and Nima Aghaeepour, both of Stanford University School of Medicine, will present how they characterized the molecular pacemakers of pregnancy by integrating CyTOF analysis and other omic approaches on blood samples collected throughout pregnancy.

      The maintenance of pregnancy relies on a finely tuned immune balance between tolerance to the fetal allograft and protective mechanisms against invading pathogens. Demonstrating the chronology of immune adaptations to a term pregnancy provides the framework for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.

      Dr. Gaudilliere will present data demonstrating that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. An elastic net model, with prior Bayesian distributions extracted from literature-based knowledge of the immune system, was used to develop a predictive model of interrelated immune events that accurately captured the chronology of pregnancy. Model components not only highlighted known biology, such as enhanced innate immune responses during pregnancy, but revealed novel biology, including a critical role for IL-2–dependent STAT5a/b signaling pathway in modulating T cell function during pregnancy.

      Dr. Aghaeepour will describe how the immunological dataset derived from the CyTOF analysis was integrated with other omics datasets collected simultaneously at each time point, including data from the transcriptome, microbiome, proteome and metabolome. He will also present a novel computational approach combining all available omics datasets into a predictive model that reveals unique interactions between the different pacemakers of pregnancy.

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