A significant problem in the study of complex biologic phenomena or human disease is the management and comparison of large and disparate datasets. Biological and clinical measurements of signaling processes or disease progression are tied to experimental design. But single measurements of phenotypes are often insuffi- cient to provide the level of detail necessary to understand the biological complexity. Often investigators resort to several independent measurements and then attempt to correlate outcomes to either prove a hypothesis or generate a new one. The problem is compounded by the advent of technologies capable of generating ultra- large datasets containing multiple parameters per measurement. The investigators must employ and under- stand statistical regimes that allow verifiable claims to be made on a single dataset. Since its first application to characterize the human immune continuum in healthy bone marrow by the Nolan Laboratory at Stanford, high dimensional single cell mass cytometry (CyTOF) has proven over and over again to be a technology that can provide a systems-level view of the immune system in health and disease. This unprecedented level of detail has identified mechanistic readouts that may otherwise have been missed, in a variety of disease settings. Many of these readouts are now incorporated in several validation studies. The Ad- vanced Proteomic Phenotyping Core under the directorship of Dr. Garry Nolan and Associate directorship of Dr. Wendy Fantl will provide mass cytometry to the three projects comprising this TPPG and in so doing, pro- vide a systems-level view of PAH not ever seen before. CyTOF will complement the other platforms being used by the Projects that span studies about the basic biology of PAH all the way through to clinical trials with Elafin, an elastase inhibitor and immune modulator shown to have extremely promising pre-clinical results. Currently, CyTOF provides simultaneous measurements of up to 50 parameters per single cell. While this in itself allows one to uncover new facets about a disease, it becomes clear that more parameters are needed. Thus part of the Core's activities are to continue to develop new reagents to make this possible as well as to develop new technologies. For the latter, the Nolan Laboratory has developed a new single cell fluorescent- detection multiplexing imaging platform called antibody sequencing (ABSeq) that uses DNA bar-coded antibod- ies and in theory has no limit on the number of parameters that can be measured. Clearly, the value of this im- aging platform will be its application to characterize lung slides from PAH patients and correlate the findings with the CyTOF studies performed with blood samples and with multiplexed ion based imaging (MIBI) used in Projects 1 and 2. .A main component of this proposal is to provide the Projects with currently available soft- ware, developed by the Nolan Laboratory to analyze high-dimensional single-cell datasets as well as statistical regimens to ensure that meaningful conclusions are made. In addition this Core will continue to develop new algorithms that will explore and converge datasets from CyTOF and imaging platforms.

Public Health Relevance

The Advanced Proteomic Phenotyping Core will support all three projects of this TPPG by facilitating the gen- eration of valuable datasets that characterize immune variables, in samples from pulmonary arterial hyperten- sion (PAH) patients and healthy controls. The data will be generated with new cutting-edge technology plat- forms that analyze samples at the level of their individual cells in unprecedented detail. The core will contribute to an integrated database of immunological and clinical readouts on these specimens which can be mined for new metrics of immunological readouts in PAH.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Program Projects (P01)
Project #
5P01HL108797-08
Application #
9734136
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Xiao, Lei
Project Start
Project End
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
8
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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