September 24, 2014  |  User Stories  |  By  |  0 Comments

Cytobank User Stories: Gabi Fragiadakis

Welcome to Cytobank User Stories, a series featuring interviews with Cytobank users on their research, scientific vision, and use of fluorescence and mass cytometry.

This time we interview Gabi Fragiadakis, Ph.D. candidate in the Nolan Lab at Stanford University School of Medicine.

What are you excited about in science? What is your scientific vision?
Gabi Fragiadakis

Gabi Fragiadakis
Ph.D. candidate at Stanford University School of Medicine

We are in a particularly exciting time for immunology—with the advent of new technologies, we have the potential to profile the immune system as a dynamic set of interactions as opposed to looking at one piece at a time.

I believe that the ability to examine the immune system as a system will lead to vast improvements in patient monitoring and treatment.

What do you study / what is your field?
 I combine the technology of mass cytometry with analytical tools from machine learning and statistics to answer questions about human immunology. I use these tools to profile and monitor patients as they respond to immune perturbations, both acute and chronic, as well as to develop metrics of immune health.
Tell us about your most recent paper
 In collaboration with Dr. Brice Gaudilliere, Dr. Martin Angst, and my Ph.D. advisor Dr. Garry Nolan, we have used mass cytometry to track patients as they recover from surgery. We analyzed blood from 32 patients undergoing hip replacement surgery at Stanford Hospital before their surgery, and at several time points afterward. We detected consistent changes across patients in redistribution of cell subsets and activation of signaling pathways post-surgery, delineating a trauma-specific immune signature. However, these patients exhibited a wide range of recovery rates as determined by levels of fatigue, hip function, and pain. We hypothesized that early immune responses may predict patient recovery rate. When regressed against these parameters of clinical recovery, we found strong correlations with pSTAT3, pNFkB, and pCREB signaling in classical monocyte subsets. We hope these results can be used as diagnostic signatures to improve patients’ recovery [1]. The datasets are publicly available on Cytobank.
What are some of your favorite papers?
I’m inspired by Dr. Daphne Koller’s paper that uses learning algorithms to assess features of breast cancer histology that predict survival. It is an elegant application of machine learning to biology in a way that directly benefits patient care [2]. I’m additionally impressed by Mike Snyder’s work in personal omics profiling using the new technologies available to define better metrics of immune health [3].
What do you do for fun?
I love being active and trying new kinds of physical activity—some of my favorites are hiking, running, yoga, dance, and resistance training. I enjoy traveling and exploring new places, and spending time with friends and family.
What’s your favorite thing about Cytobank?
Some of my favorite features are centralized data storage and the ability to share experiments across users, which has been integral to the collaborative projects I’ve been involved in. I also very much appreciate how responsive Cytobank is to user feedback.
References
  1. Clinical Recovery from Surgery Correlates with Single-Cell Immune Signatures. Gaudilliere B and Fragiadakis G et al. Sci. Transl. Med. (2014) 10.1126/scitranslmed.3009701. [Journal]
  2. Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival. Beck AH et al. Sci. Transl. Med. (2011) 3(108): 108ra113 . [Abstract]
  3. Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes. Chen R et al. Cell (2012) 148(6): 1293–1307. [Journal]

Send us feedback and let us know who you’d like to hear from (including yourself)!

Interview conducted by Christina Hall.