Abstract

Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical diagnostics for a variety of human diseases. Traditionally, the majority of cytometry experiments have been analyzed visually, either by serial inspection of one or two dimensions (markers) at a time (a process termed """"""""gating"""""""", with boundaries or """"""""gates"""""""" defining cell populations of interest), or by very basic comparisons of summary statistics. Technological advances in cytometry based on atomic mass spectrometry will soon allow researchers to query up to 50 markers (as opposed to about 10 with current technology), making traditional analysis approaches untenable. This new mass cytometry technology will generate high-throughput high-dimensional datasets, opening up new avenues for single--cell biology. As a consequence, it is essential that analytical tools and statistical methods take part in this revolution to harness the full potential of the technology. We are proposing novel computational methods and software tools for both flow and mass cytometry. The impact of these tools will be to provide researchers with a set of tools that will become essential to extract meaningful information from such data. We will apply our methods to a number of different scenarios such as the identification of immune correlate of protections for HIV and malaria vaccines, the identification of genetic mechanisms of homeostasis, and the clinical prediction of chronic inflammatory conditions.

Public Health Relevance

The aims of this project are to develop statistical and computational methods for the analysis of flow and mass cytometry data and apply these to large datasets issued from clinical trials. The impact of these tools will be to provide researcher with a set of tools that will become essential to extract meaningful information from such data. The domain of applications spans all diseases, and our current application covers multiple diseases including HIV, malaria and inflammatory conditions.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB008400-06
Application #
8449566
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Korte, Brenda
Project Start
2008-05-01
Project End
2016-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
6
Fiscal Year
2013
Total Cost
$347,163
Indirect Cost
$98,584

  Institution

Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109

  Publications

Johnstone, Jennie; Parsons, Robin; Botelho, Fernando et al. (2017) T-Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents. J Am Geriatr Soc 65:153-159
Pedersen, Natasja Wulff; Chandran, P Anoop; Qian, Yu et al. (2017) Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells. Front Immunol 8:858
Slichter, Chloe K; McDavid, Andrew; Miller, Hannah W et al. (2016) Distinct activation thresholds of human conventional and innate-like memory T cells. JCI Insight 1:
Finak, Greg; Gottardo, Raphael (2016) Promises and Pitfalls of High-Throughput Biological Assays. Methods Mol Biol 1415:225-43
Hsiao, Chiaowen; Liu, Mengya; Stanton, Rick et al. (2016) Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure. Cytometry A 89:71-88
Fletez-Brant, Kipper; Špidlen, Josef; Brinkman, Ryan R et al. (2016) flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89:461-71
McDavid, Andrew; Finak, Greg; Gottardo, Raphael (2016) The contribution of cell cycle to heterogeneity in single-cell RNA-seq data. Nat Biotechnol 34:591-3
Finak, Greg; Langweiler, Marc; Jaimes, Maria et al. (2016) Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Sci Rep 6:20686
Aghaeepour, Nima; Chattopadhyay, Pratip; Chikina, Maria et al. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89:16-21
Courtot, Mélanie; Meskas, Justin; Diehl, Alexander D et al. (2015) flowCL: ontology-based cell population labelling in flow cytometry. Bioinformatics 31:1337-9

Showing the most recent 10 out of 48 publications