Data from: Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis

Mass, Elvira 1 ; Kayvanjoo, Amir1

Research facility: University of Bonn

Published Nov 17, 2025 on Dryad. https://doi.org/10.5061/dryad.fn2z34v00

Data files

Nov 17, 2025 version files 47.49 GB

220624_FetalLiverE145.tif

8.14 GB
Fetal_Liver_Reporter_Section_1.tif

9.67 GB
Fetal_Liver_Reporter_Section_2.tif

9.67 GB
Fetal_Liver_Revision_WT.tif

20.01 GB
README.md

8.90 KB

Abstract

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.

Access this dataset on Dryad

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
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
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

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

Download the pyramidal CODEX image file(s) from the Dryad record.
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.
Combine these images with the relevant source data spreadsheets to reproduce quantitative measurements shown in the figures (cell counts, distances, neighbourhood analyses).
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.

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⁺CD11b⁺ cells were not gated but F4/80⁺ Iba1⁺ cells. HSCs were gated as CD150⁺ c-Kit⁺ cells, erythrocytes as CD45-Ter119⁺ cells, and blood vessels as CD45-CD31⁺SMA⁺ 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.