Microenvironmental control of hematopoietic stem cell fate via CXCL8 and protein kinase C.
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Source reference: Cell reports • 2023 • DOI: 10.1016/j.celrep.2023.112528 • PMID: 37209097 Altered hematopoietic stem cell (HSC) fate underlies primary blood disorders but microenvironmental factors controlling this are poorly understood. Genetically barcoded genome editing of synthetic target arrays for lineage tracing (GESTALT) zebrafish were used to screen for factors expressed by the sinusoidal vascular niche that alter the phylogenetic distribution of the HSC pool under native conditions. Dysregulated expression of protein kinase C delta (PKC-δ, encoded by prkcda) increases the number of HSC clones by up to 80% and expands polyclonal populations of immature neutrophil and erythroid precursors. PKC agonists such as cxcl8 augment HSC competition for residency within the niche and expand defined niche populations. CXCL8 induces association of PKC-δ with the focal adhesion complex, activating extracellular signal-regulated kinase (ERK) signaling and expression of niche factors in human endothelial cells. Our findings demonstrate the existence of reserve capacity within the niche that is controlled by CXCL8 and PKC and has significant impact on HSC phylogenetic and phenotypic fate. Instructions for use: README Microenvironmental Control of Hematopoietic Stem Cell Fate via CXCL8 and Protein Kinase C This an R data package containing processed data sufficient to reproduce key figures from this publication. It must be used together with the analysis project (see below). Raw data are available in GEO as referenced in the mansucript. Steps to Reproduce Manuscript Figures 1. System Requirements R v4.2 or greater Rstudio This software has been tested on Linux Ubuntu 18.04.6 and Windows 10 Loading the complete dataset occupies approximately 7 GB memory. 2. Installation download the package tarball to your system. Do not clone this repository since it does not contain the data. You have to get it from zenodo: https://zenodo.org/communities/blaserlab/ clone the analysis project to your computer using git clone https://github.com/blaserlab/pkc_cxcl8.git open the R project by double-clicking on the pkc_cxcl8.Rproj file a list of the packages required for the project can be found in library_catalogs/blas02_pkc_cxcl8.tsv. Filter for packages with status == "active". Install these packages. install custom packages from our R Universe repository using these commands: install.packages('blaseRtools', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>')) install.packages('blaseRtemplates', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>')) install.packages('blaseRdata', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>')) edit and source R/dependencies.R typical time required for the first installation and data loading is approximately 15 minutes. This excludes the time required to download the data package. 3. Instructions for use after installing and configuring source R/dependencies.R source R/configs.R source R/make_all_figs.R. This will generate all computationally-derived figures in the manuscript. source R/supplemental_tables.R. This will generate all supplementary tables in the manuscript. open Rmd/stats.Rmd. Click on the knit dropdown menu and ensure knit directory is set to "Project Directory". Click "knit" to generate a detailed pdf statistics report to accompany the figures. If properly configured, these scripts should run to completion in 1-2 minutes. 4. Each computationally-generated figure panel is associated with processed data and code for visualization. Each processed data object has its own help manual and associated processing code within the data package. To access these resources do the following: find the variable name for the panel you wish to review in the appropriate figure composition file in R/figs/composition. search for that variable name in R/figs/staging find the original data object used to generate that panel in the code type ?data_object_name to get the help manual to review processing code, go to the installed location of pkc.cxcl8.datapkg on your system, enter the data-raw directory and run grep --include=*.R -rnw '.' -e "data_object_name"
源参考文献：《Cell Reports》• 2023年 • DOI: 10.1016/j.celrep.2023.112528 • PMID: 37209097 造血干细胞（hematopoietic stem cell, HSC）命运改变是原发性血液系统疾病的核心病理基础，但调控该过程的微环境因子目前仍鲜有报道。本研究采用用于谱系追踪的合成靶点阵列基因条形码基因组编辑（Genetically barcoded genome editing of synthetic target arrays for lineage tracing, GESTALT）技术构建的斑马鱼，筛选出窦状血管壁龛中表达的、可在原生条件下改变造血干细胞池系统发育分布的因子。研究发现，蛋白激酶Cδ（protein kinase C delta, PKC-δ，由prkcda基因编码）的表达失调可使造血干细胞克隆数量增加最多80%，并扩增未成熟中性粒细胞和红系前体的多克隆群体。CXCL8等蛋白激酶C激动剂可增强造血干细胞在壁龛中的驻留竞争能力，并扩增特定的壁龛细胞群体。CXCL8可诱导PKC-δ与黏着斑复合物结合，激活细胞外信号调节激酶（extracellular signal-regulated kinase, ERK）信号通路，并促进人内皮细胞中壁龛因子的表达。本研究结果证实，壁龛中存在由CXCL8和PKC调控的储备功能，该功能对造血干细胞的系统发育和表型命运具有显著影响。 # 数据集使用说明：README ## CXCL8与蛋白激酶C调控造血干细胞命运的微环境机制 本数据集为R语言数据包，包含可复现该论文关键图表的已处理数据，需配合配套分析项目使用（详见下文）。原始数据可参见手稿中提及的基因表达综合数据库（Gene Expression Omnibus, GEO）。 ### 复现论文图表步骤 1. **系统要求** R v4.2及以上版本 RStudio 本软件已在Ubuntu 18.04.6 Linux系统与Windows 10系统上完成测试。 完整数据集加载后约占用7GB内存。 2. **安装步骤** 1. 将数据包tar压缩包下载至本地系统，请勿直接克隆本仓库，因其中未包含数据文件。需从Zenodo社区获取：https://zenodo.org/communities/blaserlab/ 2. 使用git克隆分析项目至本地：`git clone https://github.com/blaserlab/pkc_cxcl8.git` 3. 双击`pkc_cxcl8.Rproj`文件打开R项目 4. 项目所需的全部软件包列表可在`library_catalogs/blas02_pkc_cxcl8.tsv`中查看，筛选`status == "active"`的软件包并完成安装。 5. 通过以下命令从本团队的R宇宙仓库安装自定义软件包： r install.packages('blaseRtools', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) install.packages('blaseRtemplates', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) install.packages('blaseRdata', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) 6. 编辑并运行`R/dependencies.R`脚本。 首次安装与数据加载的典型耗时约为15分钟（不包含下载数据包的时间）。 3. **安装配置完成后的使用说明** 1. 运行`source R/dependencies.R` 2. 运行`source R/configs.R` 3. 运行`source R/make_all_figs.R`，即可生成论文中所有计算衍生图表。 4. 运行`source R/supplemental_tables.R`，即可生成论文中所有补充表格。 5. 打开`Rmd/stats.Rmd`文件，点击编织下拉菜单并确认编织目录设置为“项目目录”，点击“编织”即可生成伴随图表的详细PDF统计报告。 若配置无误，上述脚本可在1~2分钟内运行完成。 4. **单个计算生成图表面板的资源访问方式** 每个计算生成的图表面板均关联了已处理数据与可视化代码。每个已处理数据对象均配有独立的帮助手册，且在数据包中包含对应的处理代码。可通过以下方式查阅相关资源： 1. 在`R/figs/composition`目录下的对应图表组成文件中，找到需查阅的面板对应的变量名。 2. 在`R/figs/staging`目录中搜索该变量名。 3. 在代码中找到生成该面板所用的原始数据对象。 4. 输入`?data_object_name`即可获取该数据对象的帮助手册。 5. 若需查阅处理代码，可进入系统中`pkc.cxcl8.datapkg`的安装目录，进入`data-raw`目录后执行以下命令搜索对应处理代码： bash grep --include=*.R -rnw '.' -e "data_object_name"
