Systems biology of macrophage activation and plasticity

巨噬细胞激活和可塑性的系统生物学

• 批准号: 8946514
• 负责人: John Tsang
• 金额: $ 53.78万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8946514
• 关键词: Address; Adopted; Alternative Splicing; Atherosclerosis; Autoimmunity; Binding; Biological Assay; Biological Models; Blood; Cells; ChIP-seq; Chromatin; Code; Collaborations; Complement; Complex; Computing Methodologies; Data; Data Analyses; Data Set; Decision Making; Detection; Disease; Dose; Enhancers; Environment; Event; Exhibits; Flow Cytometry; Functional RNA; Gene Expression; Gene Expression Profile; Gene Proteins; Genes; Genomics; Goals; Health; Hematopoietic; Heterogeneity; Host Defense; Human; Human Cell Line; Immune; Individual; Infection; Investigation; Lead; Ligands; Macrophage Activation; Malignant Neoplasms; Measures; Methods; MicroRNAs; Modeling; Molecular; Myelogenous; Noise; Obesity; Pathology; Pathway interactions; Pattern; Phenotype; Play; Population; Process; Protein Isoforms; RNA Splicing; Reading; Rest; Role; Sampling; Signal Transduction; Stimulus; Stress; System; Systems Biology; T-Lymphocyte; Testing; Time; Tissues; Transcript; Transcriptional Regulation; base; cytokine; design; digital; in vivo; information processing; interest; macrophage; network architecture; response; single cell analysis; transcription factor; transcriptomics; trend

1. Apply experimental genomics approaches to measure global responses before and after perturbations and use (and develop if necessary) computational approaches to process and integrate such data to systematically infer networks. Our initial focus is on transcripomic phenotypes including that of microRNAs. We are using a combination of RNAseq, microarray and Fluidigm the former can provide detailed transcriptomic information including the abundance of non-coding genes, alternative splicing isoforms and rare transcripts; microarrays can be applied to a larger number of conditions/samples because they are less expensive; Fluidigm offers inexpensive profiling of a large number of samples by focusing on a carefully selected panel of transcripts. Some example efforts include: a. In collaboration with the Germain and Fraser labs, we have assayed the transcriptomic and microRNA responses of BMDMs in response to dose combinations of TLR ligands. We are particularly interested in integrating this with signaling level data to infer the connection between signaling events to transcriptional regulation. b. In another project we stimulated RAW cells with LPS (a prototypical TLR activator) and generated RNAseq data with deep coverage (160 million reads per sample). Using this data, we have developed computational pipelines for processing and analyzing RNAseq data and have examined basal and LPS-stimulated phenotypes at the splice isoform (both conserved and non-conserved), intergenic, and rare transcript levels. In addition to gene expression, we are also in the process of developing experimental and computational approaches to assay chromatin states, including transcription factor binding data. During this past year by utilizing the deep coverage provided by RNAseq, we have developed computational approaches to characterize the extent of non-conserved unexpected splicing events (USE), many of which have postulated to be results of noisy splicing. We found that USEs are prevalent across macrophages and T cells in both resting and stimulated conditions (in collaboration with the Jun Zhu lab on the T cell data). The extent of USE is highly variable across genes, with certain pathway exhibiting significantly different levels of USEs. We further tested our approach using public data sets obtained from multiple human cell lines and observed similar trends. We also observed that that some USE events are conserved across conditions while some are condition specific. We are in the process of utilizing USE as a means to infer factors and regulatory networks that regulate alternative splicing in cells. c. We have generated RNAseq data on both coding and non-coding RNAs across diverse macrophage activation conditions. These data are being used to understand the transcriptomic landscape and splicing repertoire of macrophage states; they are also being integrated with other data types, such as ChIP-Seq. 2. Develop single-cell gene expression assays to measure the transcriptome in individual cells before and after perturbations. We have been developing two complementary approaches. In the first we combine flow cytometry with single-cell based PCR (Fluidigm) to assay the expression of 100 genes/proteins. Using human blood derived macrophage as a model, we have generated data on hundreds of single cells from different stimulation conditions. We are particularly interested in assaying expression heterogeneity under different stimulations and time-points because we would like to compare environmentally induced differences both at the cell population and individual cell levels in macrophage activation. Example questions include: How heterogeneous are the responses? Is the level of heterogeneity different across stimulations? Can transcriptomic heterogeneity be used to inform the underlying network architecture? In addition to single cells, we are generating data at the bulk as well as tens-of-cells levels to complement and augment single cell data. To utilize Fluidigm, we have developed a strategy to design informative, unique marker panels based on multiple public gene expression data sets derived from myeloid as well as other hematopoietic cells. As a complementary approach, we have also been testing experimental strategies to perform RNAseq on single cells. This is challenging partially because of limit of detection issues and quantitation accuracy, especially for lowly abundant transcripts. To address these issues, we have adopted a barcoding strategy that mimic the principle used in digital PCRs. 3. We have been using ChIP-Seq and CAGE-Seq approaches to assay chromatin, enhancers and transcription-factor binding states in human macrophages before and after different types of perturbations. These data are being integrated with gene expression, both at the bulk and single-cell levels, to dissect regulatory networks within and across macrophage activation states. 4. Develop computational data analysis approaches and methods. In addition to computational methods for processing and analyzing individual data types, a key goal is to integrate the data obtained from different perturbation conditions to infer gene network(s). Since in vivo stimulations typically involve combinations of cytokines and molecular patterns, one question we aim to address is whether responses to complex stimuli can be predicted and understood based on responses to simpler constituent stimulus. For example, by using data from AfCS and data generated by our and the Fraser labs, we are developing a computational approach to predict genes and proteins that facilitate cross-talk between signaling subnetworks. This approach can also lead to better understandings of how networks evolve to process complex information and general network features for generating phenotypic diversity. Another focus is on the analysis of single cell data, particularly how to estimate heterogeneity in the presence of noise and detection limits.

1。应用实验基因组学方法来测量扰动前后的全局响应，并使用（并在必要时开发）计算方法来处理和集成此类数据以系统地推断网络。我们最初的重点是转界表型，包括microRNA的表型。我们正在使用RNASEQ，微阵列和流体的组合，前者可以提供详细的转录组信息，包括丰富的非编码基因，替代剪接同工型和稀有的转录本；微阵列可以应用于较大的条件/样品，因为它们便宜； Fluidigm通过专注于精心选择的成绩单来提供大量样品的廉价分析。一些例子包括： 一个。 与Germain和Fraser Labs合作，我们根据TLR配体的剂量组合分析了BMDMS的转录组和microRNA响应。我们特别有兴趣将其与信号级别数据集成在一起，以推断信号事件与转录调控之间的连接。 b。 在另一个项目中，我们用LP（原型TLR激活剂）刺激了RAW细胞，并生成了具有深层覆盖范围的RNASEQ数据（每个样品读取1.6亿读）。使用这些数据，我们开发了用于处理和分析RNASEQ数据的计算管道，并在剪接同工型（保守和非保存），基因间和罕见的转录水平上检查了基础和LPS刺激的表型。除基因表达外，我们还在开发实验和计算方法以测定染色质状态，包括转录因子结合数据。在过去的一年中，通过利用RNASEQ提供的深层覆盖范围，我们开发了计算方法来表征未经保留的意外剪接事件（使用）的程度，其中许多假定是嘈杂的剪接结果。我们发现，在静止和刺激条件下，在巨噬细胞和T细胞之间普遍存在（与T细胞数据上的Jun Zhu实验室合作）。使用程度在各个基因之间是高度变化的，某些途径表现出明显不同的用途水平。我们使用从多个人类细胞系获得的公共数据集进一步测试了我们的方法，并观察到了相似的趋势。我们还观察到，某些使用事件是在条件上保存的，而有些则特定于条件。我们正在利用使用作为推断细胞中替代剪接的因素和调节网络的手段。 c。 我们已经在各种巨噬细胞激活条件下生成了有关编码和非编码RNA的RNASEQ数据。这些数据用于了解巨噬细胞状态的转录组景观和剪接曲目；它们还与其他数据类型（例如chip-seq）集成在一起。 2。开发单细胞基因表达测定法以测量扰动前后单个细胞中的转录组。我们一直在开发两种互补方法。在第一个中，我们将流式细胞术与单细胞基PCR（流体）结合在一起，以测定100个基因/蛋白质的表达。使用人类血液衍生的巨噬细胞作为模型，我们对来自不同刺激条件的数百个单个细胞产生了数据。我们特别有兴趣在不同的刺激和时间点下测定表达异质性，因为我们想比较巨噬细胞激活中细胞群和单个细胞水平的环境诱导的差异。示例问题包括：响应的异质性如何？异质性水平在刺激之间有不同吗？转录组异质性可以用于告知基础网络体系结构吗？除了单个单元外，我们还在批量以及数十个电池级别生成数据以补充和增强单细胞数据。为了利用流体化，我们已经制定了一种策略来设计基于髓样和其他造血细胞的多个公共基因表达数据集设计信息，独特的标记面板。作为一种互补方法，我们还一直在测试在单细胞上执行RNASEQ的实验策略。这是由于检测问题的限制和定量准确性的限制，特别是对于较低的成绩单而言，这是具有挑战性的。为了解决这些问题，我们采用了一种模拟数字PCR中原理的条形码策略。 3。我们一直在使用Chip-Seq和Cage-Seq方法来测定染色质，增强子和转录因子结合态在不同类型的扰动之前和之后的人类巨噬细胞中。这些数据正在与基因表达（无论是在大容量和单细胞水平上）集成，以剖析巨噬细胞激活状态内和跨巨噬细胞的调节网络。 4。开发计算数据分析方法和方法。 除了用于处理和分析各个数据类型的计算方法外，关键目标是整合从不同扰动条件获得的数据以推断基因网络。由于体内刺激通常涉及细胞因子和分子模式的组合，因此我们旨在解决一个问题是，是否可以根据对简单组成刺激的反应来预测和理解对复杂刺激的反应。例如，通过使用来自AFCS的数据以及我们和Fraser Labs生成的数据，我们正在开发一种计算方法来预测基因和蛋白质，以促进信号子网络之间的串扰。这种方法还可以更好地理解网络如何发展以处理复杂的信息和一般网络特征以生成表型多样性。另一个重点是分析单细胞数据，尤其是在存在噪声和检测极限的情况下如何估计异质性。