DOI: 10.5281/zenodo.18623891
title: Microenvironmental control of hematopoietic stem cell fate via CXCL8 and protein  kinase C.

description: 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"