Core B: Advanced Proteomic Phenotyping Core

在研究复杂生物现象或人类疾病中的一个重要问题是管理和 比较大型和不同的数据集。信号传递过程的生物和临床测量或 疾病的进展与实验设计密切相关。但对表型的单一测量往往是不够的- 提供了解生物复杂性所需的详细程度。调查人员经常求助于 到几个独立的测量，然后尝试将结果关联起来，以证明假设或 生成一个新的。这一问题因能够产生超临界气体的技术的出现而变得更加复杂。 每次测量包含多个参数的大数据集。调查人员必须雇用并低于- 建立统计制度，允许在单一数据集上做出可核实的声明。 自Nolan首次应用于描述健康骨髓中的人类免疫连续体以来 斯坦福大学实验室的高维单细胞质量细胞术(CyTOF)一再证明 成为一项能够提供健康和疾病中免疫系统的系统级视图的技术。这 前所未有的详细程度已经确定了原本可能被错过的机械性读数，在一个 多种多样的疾病设置。这些读数中的许多现在被纳入了几项验证研究。广告- 在加里·诺兰博士和副董事的领导下，先进的蛋白质组表型核心 Wendy Fantl博士将为组成这个TPPG的三个项目提供质量细胞仪，并在这样做的过程中，支持- 从系统级别查看以前从未见过的PAH。CyTOF将与其他平台形成互补 由多环芳烃的基础生物学研究一直到临床试验的项目使用 弹性蛋白酶抑制剂和免疫调节剂Elafin被证明具有非常有希望的临床前结果。 目前，CyTOF为每个单细胞提供多达50个参数的同时测量。在这段时间里 它本身可以让人们发现一种疾病的新方面，但很明显，需要更多的参数。 因此，核心的部分活动是继续开发新的试剂，以使这成为可能，以及 开发新技术。对于后者，诺兰实验室已经开发出一种新的单细胞荧光-- 名为抗体测序(ABSeq)的检测多路成像平台使用DNA条码抗BOD- IES和理论上对可以测量的参数数量没有限制。很明显，这件事的价值在于- 衰老平台将被应用于表征PAH患者的肺切片并将结果相互关联 通过对血液样本和使用基于多路离子的成像(MIBI)进行的CyTOF研究 项目1和2.这项建议的一个主要组成部分是为项目提供目前可用的软件-- Ware，由诺兰实验室开发，用于分析高维单细胞数据集以及统计 养生计划，以确保得出有意义的结论。此外，该核心将继续开发新的 将探索和融合来自CyTOF和成像平台的数据集的算法。

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.