Our laboratory conceived and developed the initial flow cytometry instruments and has since introduced many innovations that increase the broad utility and accessibility of this technology. As part of this effort, we developed key software an analysis capabilities that are now ubiquitous among research and clinical users of flow technology, among whom HIV researchers and clinicians clearly predominate. In studies here, we propose to exploit our long-term flow expertise in the interests of introducing much needed flow capabilities to facilitate high normal and high-throughput work HIV trial samples and samples analyzed during vaccine development Basically, we propose to develop innovative methods to automate data handling and processing tasks for flow data. This will include the application of our recently published methods for locating and identifying cells in subsets of interest, so that we can do statistically robust comparisons of subset marker expression in/on these subsets. These methods will also allow us to develop innovative methods for identifying individual or groups of marker changes stimulated by antigen or other stimulation of frozen or freshly collected samples from trial and/or other subjects. Importantly, this software is intended to provide (for the first time) a robust single measure of non-coordinated changes in expression of several intracellular cytokines induced in T cell subsets stimulated in vivo or ex vivo in vaccine development models. Because of its design, we propose to create the software we build for these tasks in an integrated format that is readily amenable to automation to enable high-throughput analyses.

Public Health Relevance

We propose to develop innovative methods to automate key handling and data processing tasks needed for direct and automated analysis of flow cytometry data collected for samples from HIV studies and vaccine trial. Once established, this software will also be useful for HIV clinical practice. Importantly, the work we propose will introduce entirely new statistics-based methods for identification of T cell subsets and the application of entirely new statistical methods to compare changes in subset marker expression induced by stimulation of samples from vaccine trials and other HIV studies. Unlike all current methods, these new methods will allow computation of a single index of response to stimulation, even when the response being measured requires integration of data uncoordinated changes that occur for several markers in or on T cell subsets.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI098519-03
Application #
8636991
Study Section
Special Emphasis Panel (ZRG1-AARR-K (90))
Program Officer
Warren, Jon T
Project Start
2012-04-06
Project End
2015-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
3
Fiscal Year
2014
Total Cost
$240,250
Indirect Cost
$71,250
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Meehan, Stephen; Walther, Guenther; Moore, Wayne et al. (2014) AutoGate: automating analysis of flow cytometry data. Immunol Res 58:218-23