DOI: 10.5061/dryad.fn2z34v00
title: Data from: Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis

description: During embryogenesis, the fetal liver becomes the main hematopoietic
 organ, where stem and progenitor cells as well as immature and mature
 immune cells form an intricate cellular network. Hematopoietic stem cells
 (HSCs) reside in a specialized niche, which is essential for their
 proliferation and differentiation. However, the cellular and molecular
 determinants contributing to this fetal HSC niche remain largely unknown.
 Macrophages are the first differentiated hematopoietic cells found in the
 developing liver, where they are important for fetal erythropoiesis by
 promoting erythrocyte maturation and phagocytosing expelled nuclei. Yet,
 whether macrophages play a role in fetal hematopoiesis beyond serving as a
 niche for maturing erythroblasts remains elusive. Here, we investigate the
 heterogeneity of macrophage populations in the fetal liver to define their
 specific roles during hematopoiesis. Using a single-cell omics approach
 combined with spatial proteomics and genetic fate-mapping models, we found
 that fetal liver macrophages cluster into distinct yolk sac-derived
 subpopulations and that long-term HSCs are interacting preferentially with
 one of the macrophage subpopulations. Fetal livers lacking macrophages
 show a delay in erythropoiesis and have an increased number of
 granulocytes, which can be attributed to transcriptional reprogramming and
 altered differentiation potential of long-term HSCs. Together, our data
 provide a detailed map of fetal liver macrophage subpopulations and
 implicate macrophages as part of the fetal HSC niche. 
description: 5µm slices of fetal liver from E14.5 wildtype embryos were
 prepared and used for CODEX staining following the manufacturer’s
 instructions. Briefly, sections were retrieved from the freezer, let dry
 on drierite beads, and fixed for 10 min in ice-cold acetone (Sigma
 Aldrich, St. Louis, MO, USA). After fixation, samples were rehydrated and
 photobleached twice as described in (Du, Lin et al. 2019). Following
 photobleaching, sections were blocked and stained with a 20-plex CODEX
 antibody panel (Table S5 and S6) overnight at 4 °C. After staining,
 samples were washed, fixed with ice-cold methanol, washed with 1x PBS, and
 fixed for 20 min with BS3 fixative (Sigma Aldrich, St. Louis, MO, USA). A
 final washing step with 1x PBS was performed. A
 multicycle CODEX experiment was performed following the manufacturer’s
 instructions. Images were acquired with a Zeiss Axio Observer widefield
 fluorescence microscope using a 20x objective (NA 0.85) and z-spacing of
 1.5µm. The 405, 488, 568, and 647 nm channels were used. After imaging,
 raw files were exported using the CODEX Instrument Manager (Akoya
 Biosciences, Marlborough, MA, USA) and processed with CODEX Processor v1.7
 (Akoya Biosciences). Image processing included background subtraction
 using the DAPI signals of the first and last empty cycles of the
 acquisition, deconvolution, shading correction, and stitching. For cell
 segmentation, DAPI counterstain was used for object detection, whereas
 sodium-potassium ATPase antibody staining was used as a membrane marker
 for delineating the cell shape. A manual cell
 classification was performed in CODEX MAV 1.5 (Akoya Biosciences).
 Annotation of the macrophage clusters was done using the same gating
 strategy as in flow cytometry, with the difference that
 F4/80<sup>+</sup>CD11b<sup>+</sup> cells were not
 gated but F4/80<sup>+</sup> Iba1<sup>+</sup>
 cells. HSCs were gated as CD150<sup>+</sup>
 c-Kit<sup>+</sup> cells, erythrocytes as
 CD45-Ter119<sup>+</sup> cells, and blood vessels as
 CD45-CD31<sup>+</sup>SMA<sup>+</sup> cells. After
 cell classification, Voronoi diagrams were generated in CODEX MAV using
 the four macrophage clusters, blood vessels, and HSCs as seeds.
 LogOddRatio analysis for spatial interactions was performed in
 CODEX MAV. For this, after cell classification, the four macrophage
 populations, HSCs, and blood vessels were selected. The selected minimum
 and maximum distances of interaction were 5 and 50 µm,
 respectively. For cellular neighborhood analyses, the
 .csv files generated with CODEX MAV were exported to CytoMAP (Stoltzfus,
 Filipek et al. 2020) and the same cell classification was used to annotate
 the cells. A raster scan with a radius of 50µm was performed to spatially
 segment the image. To define the cellular neighborhoods based on local
 composition, a self-organizing map (SOM) clustering algorithm was used,
 considering only the macrophage populations. Heatmaps were generated to
 determine the cell composition of each neighborhood. To measure the
 distances between macrophage clusters and erythrocytes, images were
 exported to QuPath v0.3. and cells were detected using DAPI signals.
 Single object classifiers for each marker were trained, and these were
 used to generate composite classifiers to identify macrophage populations,
 erythrocytes, and HSCs as before. The distance between the cells of each
 macrophage cluster and their closest erythrocyte was measured and
 plotted. To validate the proximity of macrophage
 clusters to HSCs, images were exported to QuPath v0.3, cells were
 segmented, and HSCs were identified, as described above. A circle with a
 fixed radius of 50µm was drawn and centered on 20 randomly selected HSCs.
 Next, the same composite classifiers to identify macrophage clusters were
 applied to the annotated circles, and the number of cells of each
 macrophage cluster within the defined radius was counted.
description: # Data from: Fetal liver macrophage imaging and transcriptomic datasets
 [Access this dataset on Dryad](https://doi.org/10.5061/dryad.fn2z34v00)
 This dataset accompanies the article *“Fetal liver macrophages contribute
 to the hematopoietic stem cell niche by controlling granulopoiesis”*
 (Kayvanjoo et al., eLife 2024). It brings together multiplexed tissue
 imaging (CODEX) of mouse fetal liver at embryonic day (E)14.5 and
 quantitative source data for the main figures. The data capture the
 spatial distribution and phenotypic heterogeneity of fetal liver
 macrophage subpopulations, their physical proximity to long‑term
 hematopoietic stem cells (LT‑HSCs), and downstream effects on
 erythropoiesis and granulopoiesis. The Dryad record contains the raw CODEX
 image data and associated analytical outputs. RNA‑seq data from bulk and
 single‑cell experiments are hosted separately at GEO (GSE225444) and are
 referenced here so that users can combine imaging, flow‑cytometry and
 transcriptomic layers when reproducing the analyses. --- ## Description of
 the data and file structure ### 1. CODEX imaging data (Dryad) The core of
 this dataset is a pyramidal multi‑resolution image file (or files)
 generated using the CODEX multiplexed tissue imaging platform on E14.5
 mouse fetal liver sections. Each image contains multiple antibody channels
 plus nuclear and structural stains, enabling the identification of fetal
 liver macrophage subpopulations, erythroblasts and LT‑HSCs in situ.
 Typical contents of the imaging file(s): * Multichannel, tiled fetal liver
 sections acquired at high magnification. * One channel per antibody or
 stain (e.g. macrophage markers such as F4/80, Tim4, CD169; adhesion
 molecules; HSC markers such as CD150). * Pyramidal levels that allow
 zooming from whole‑organ context down to single‑cell resolution. *
 Embedded spatial metadata (pixel size, magnification, tile positions)
 suitable for viewing in digital pathology software. The raw CODEX image
 files are stored in pyramidal `.tif` (or OME‑TIFF) format and can be
 opened with: * **QuPath** (for digital pathology and tissue segmentation).
 * **ImageJ/FIJI** with the Bio‑Formats plugin. * Other OME‑TIFF–compatible
 viewers or analysis environments. Because of file size constraints, the
 original acquisition `.czi` files are **not** included in the Dryad
 archive; they are available from the corresponding author upon request as
 described in the article. ### 2. Figure source data spreadsheets (article)
 In addition to the image files, quantitative source data for the main and
 supplementary figures are provided as spreadsheet files (Excel `.xlsx`) as
 supplementary files to the article. These tables mirror the “Source data”
 files linked from the eLife article and can be used to regenerate plots or
 to perform secondary analyses. The spreadsheets include, for example: *
 **Figure 1—source data 1**\ Quantification of macrophage fate‑mapping
 experiments and flow‑cytometry clusters of CD11b^low/+ myeloid cells at
 E14.5.\ Typical columns include embryo identifier, genotype, macrophage
 cluster identity, count per cluster and derived frequencies. * **Figure
 3—source data 1**\ Distances between CD150^+ and CD150^− cells and Iba1^+
 macrophages measured in CODEX images.\ Typical columns include cell type
 category, embryo or image ID, distance in micrometres and statistical
 grouping variables. * **Figure 4—source data 1**\ Quantification of
 macrophage clusters within a defined radius around LT‑HSCs in CODEX
 datasets.\ Typical columns include HSC ID, macrophage cluster identity,
 number of neighbouring cells, and per‑embryo summary statistics. *
 **Figure 5—source data 1**\ Flow‑cytometry counts of erythroid
 differentiation stages in wild‑type and macrophage‑depleted embryos at
 E14.5.\ Typical columns include embryo ID, genotype, erythroid maturation
 stage and absolute or relative cell counts. * **Figure 6—source data 1**\
 Cell numbers from bulk and colony‑forming assays performed on LT‑HSCs
 isolated from control and knockout fetal livers.\ Typical columns include
 sample ID, genotype, assay type (e.g. CFU replicate, proliferation
 readout), and cell number per well or condition. * **Figure 7—source data
 1**\ Flow‑cytometry counts for stem and progenitor populations and
 adoptive transfer experiments, including granulocyte accumulation.\
 Typical columns include embryo ID, genotype, progenitor population
 identity, and absolute/relative counts or frequencies. Exact column names
 follow the conventions used in the article and are documented in the
 header rows of each spreadsheet. ### 3. External RNA‑seq data (GEO) The
 imaging dataset is designed to be used together with publicly archived
 RNA‑seq data: * **GSE225444 (GEO)** – bulk RNA‑seq and single‑cell RNA‑seq
 data. * Bulk RNA‑seq: LT‑HSCs sorted from wild‑type and
 macrophage‑depleted fetal livers at E14.5, used to assess transcriptional
 rewiring and granulopoiesis‑related gene expression changes. * Single‑cell
 RNA‑seq: CD11b^low/+ fetal liver myeloid cells profiled to define
 macrophage subpopulations and their developmental trajectories. The GEO
 record contains raw sequence files (FASTQ) and processed count matrices as
 well as sample‑level metadata (genotype, developmental stage, cell type or
 cluster annotations). These files are not duplicated in Dryad; users
 should download them directly from GEO and combine them with the CODEX
 dataset as needed. --- ## Sharing / Access information **Primary imaging
 dataset (Dryad):** * Dryad DOI:
 [https://doi.org/10.5061/dryad.fn2z34v00](https://doi.org/10.5061/dryad.fn2z34v00) * Contents: pyramidal CODEX image file(s) of E14.5 fetal livers and associated analysis outputs needed to reproduce the imaging‑based figures. **Associated transcriptomic data (GEO):** * GEO accession: [GSE225444](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE225444) * Contents: bulk RNA‑seq and single‑cell RNA‑seq datasets used for macrophage clustering, ligand–receptor analyses and LT‑HSC differential expression. **Primary publication:** * Kayvanjoo AH et al. (2024). *Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis.* eLife 13:e86493. [https://doi.org/10.7554/eLife.86493](https://doi.org/10.7554/eLife.86493) Please cite both the eLife article and the Dryad dataset (and GEO accession, if used) when using these data in your own work. --- ## Code / Software The analyses associated with this dataset combine image processing, flow‑cytometry analysis and RNA‑seq analysis. The following software environments and tools were used, as described in the Methods section of the article: * **R (≥ 4.x)** * `DESeq2` for bulk RNA‑seq differential expression analysis. * `EnhancedVolcano` for visualization of differential expression results. * Additional packages for statistics, plotting and data handling (e.g. `ggplot2`, `dplyr`, `tidyr`). * **Single‑cell RNA‑seq analysis** * Standard pipelines in R or Python (e.g. Seurat or Scanpy) for clustering, dimensionality reduction (including UMAP) and trajectory inference (e.g. PAGA) as described in the article. * **Imaging and digital pathology** * QuPath and/or ImageJ/FIJI (with Bio‑Formats) for viewing and annotating CODEX images, extracting cell‑level features and exporting distance and neighbourhood measurements. * CODEX‑specific acquisition and preprocessing software as described in Black et al., Nature Protocols 2021. * **Flow‑cytometry analysis** * Flow‑cytometry software (e.g. FlowJo or similar) and R‑based pipelines for high‑dimensional clustering and visualization. All software versions and parameter choices used for the published analyses are documented in the **Materials and methods** section of the eLife article. Users who wish to exactly reproduce the figures should follow the workflows described there, using the CODEX imaging data from Dryad together with the RNA‑seq and source data tables linked above. --- ### How to get started 1. Download the pyramidal CODEX image file(s) from the Dryad record. 2. Open the image(s) in QuPath or ImageJ/FIJI to explore the spatial organization of fetal liver macrophage subpopulations and their proximity to LT‑HSCs. 3. Combine these images with the relevant source data spreadsheets to reproduce quantitative measurements shown in the figures (cell counts, distances, neighbourhood analyses). 4. For integrated analyses, obtain the RNA‑seq datasets from GEO (GSE225444) and follow the gene‑expression workflows described in the original publication. This README is intended as a high‑level guide to the dataset structure. Users should consult the eLife article for detailed experimental protocols and analysis steps.