1. Developmental Biology
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EKLF/KLF1 expression defines a unique macrophage subset during mouse erythropoiesis

  1. Kaustav Mukherjee
  2. Li Xue
  3. Antanas Planutis
  4. Merlin Nithya Gnanapragasam
  5. Andrew Chess
  6. James J Bieker  Is a corresponding author
  1. Department of Cell, Developmental, and Regenerative Biology, Mount Sinai School of Medicine, United States
  2. Black Family Stem Cell Institute, United States
  3. Tisch Cancer Institute, United States
  4. Mindich Child Health and Development Institute, Mount Sinai School of Medicine, United States
Research Article
Cite this article as: eLife 2021;10:e61070 doi: 10.7554/eLife.61070
9 figures, 4 tables, 2 data sets and 7 additional files


Figure 1 with 1 supplement
Gene expression comparison of fetal liver (FL) F4/80+ macrophages with extensively self-renewing erythroblasts (ESREs; Erythr) and adult spleen (Spl) F4/80+ macrophages showing unique gene expression in F4/80+ FL macrophages.

(A) Hierarchical clustering dendrogram using scaled Z-scores based on the expression of the top 10,000 highly expressed genes is shown for individual RNA-Seq biological replicates from each cell type (source data: Figure 1—source data 1). (B) Principal component analysis of the cell types is plotted showing principal components 1 and 2 for each biological replicate (source data: Figure 1—source data 1). (C) Macrophage-specific or erythroid-specific marker expression in the cell types is shown, with replicates averaged together (source data: Figure 1—source data 3). (D) k-means clustering of individual RNA-Seq biological replicates of the different cell types (ESREs, Erythr; fetal liver, FL; spleen, Spl) by log2 FPKM displayed as a heatmap (source data: Figure 1—source data 4). Flower bracket indicates the gene cluster with enriched expression in F4/80+ FL macrophages. (E) Heatmap of only the uniquely expressed genes in F4/80+ FL macrophages that define the signature genes of this cell type (source data: Figure 1—source data 2). A few representative signature gene names are displayed.

Figure 1—source data 1

Scaled Z-scores of FPKM values of the top 10,000 highly expressed genes in each cell type shown in Figure 1.

Figure 1—source data 2

Expression of signature genes of fetal liver F4/80+ macrophages in each cell type.

Figure 1—source data 3

List of macrophage and erythroid markers and their expression levels in each cell type.

Figure 1—source data 4

Log2 FPKM values of all expressed genes in the cell types shown in Figure 1.

Figure 1—figure supplement 1
Isolation of a pure population of F4/80+ E13.5 fetal liver cells by FACS.

(A) Gating strategy used to stringently sort singlets that are F4/80+ is shown from unstained (no antibody [Ab]) or treated (anti-F4/80 Ab) fetal liver cells from EKLF+/+ E13.5 fetal livers. (The same approach was used for EKLF-/- cell sorting in Figure 3A.) (B) FACS-sorted cells using the strategy above were cytospun on a slide and observed after May-Grunwald-Giemsa staining for singlets, doublets, or >3 cells. 25 µm scale bars are indicated. (C) Quantification of data from (B) (source data: Figure 1—figure supplement 1—source data 1 ).

Figure 1—figure supplement 1—source data 1

Quantification of cells isolated by FACS sorting F4/80+ macrophages from E13.5 fetal liver after cytospin.

EKLF/Klf1 is expressed in fetal liver macrophages during development.

(A) Immunofluorescence tests with anti-EKLF (white), 4′,6-diamidino-2-phenylindole (DAPI) (blue), and anti-F4/80 (red) antibodies in E13.5 fetal liver cells. (A) White arrowheads show coexpression of EKLF and F4/80 proteins in single cells (representative of over 20 EKLF+/F4/80+ cells in this field of 300 cells); red arrow shows that not all F4/80+ cells are EKLF+. (B) White arrow shows that not all EKLF+ cells are F4/80+ , as expected from the FACS data (cytoplasmic EKLF signal is expected [Quadrini et al., 2008; Schoenfelder et al., 2010]). (C) Collated RNA-Seq data (Mass et al., 2016) of sorted macrophage cells from multiple staged embryonic (E) day 10.25–16.5 fetal livers or postnatal (P) day 2–21 livers (ckit-/CD45+/F480+/AA4.1-/CD11b+; n = 24 samples) show transient and abundant Klf1 reads (UCSC Genome Browser). (D) Same analysis as (C) showing RNA-Seq reads of the gene encoding F4/80 (Adgre1) as a positive control across all samples.

EKLF-dependent gene expression in fetal liver (FL) macrophages.

(A) A representative yield of cells from EKLF-/- FL sorted by F4/80 expression, used for RNA-Seq analysis, is shown (compare to WT yield in Figure 1—figure supplement 1A). (B) k-means clustering of absolute log2 FPKM of F4/80+ EKLF+/+ and F4/80+ EKLF-/-, and log2 FKPM ratio EKLF-/-(KO)/WT is displayed as a heatmap. Flower bracket indicates downregulated genes. (C) Differentially expressed genes in EKLF-/- (KO) compared to WT shown as a volcano plot (source data: Figure 3—source data 1).

Figure 3—source data 1

Differential expression test results obtained from DESeq2 using the RNA-Seq data from EKLF+/+ and EKLF-/- fetal liver F4/80+ macrophages.

Figure 4 with 1 supplement
Comparison of gene expression in F4/80+ EKLF/GFP+ and F4/80+ EKLF/GFP- fetal liver macrophages.

(A) Principal component analysis using scaled Z-score based on the expression level of the top 10,000 highly expressed genes from RNA-Seq replicates of F4/80+ EKLF/GFP+ and F4/80+ EKLF/GFP- is plotted with each axis depicting the two major principal components (source data: Figure 4—source data 1). (B) Scatterplot showing the significantly enriched genes in the F4/80+ EKLF/GFP+ population compared to F4/80+ EKLF/GFP-. Vcam1, Klf1, and Epor are highlighted in blue (source data: Figure 4—source data 2). Fragments per kilobase million (FPKM) values of (C) EKLF/Klf1 and Vcam1 and (D) Epor in the two populations.

Figure 4—source data 1

Coordinates of principal components 1 and 2 corresponding to each replicate of EKLF/GFP+ and EKLF/GFP- RNA-Seq data.

Figure 4—source data 2

Differential gene expression results obtained using DESeq2 from the EKLF/GFP RNA-Seq dataset.

Figure 4—figure supplement 1
Comparison of gene expression in F4/80+ EKLF/GFP+ and F4/80+ EKLF/GFP- fetal liver macrophages.

(A) Correlation analysis of Z-score transformed gene expression data for each replicate in RNA-Seq showing high correlation between EKLF/GFP+ and EKLF/GFP- replicates, respectively (source data: Figure 4—figure supplement 1—source data 1). (B) Volcano plot showing genes enriched in EKLF/GFP+ and EKLF/GFP- (source data: Figure 4—source data 2).

Figure 4—figure supplement 1—source data 1

Scaled Z-scores of FPKM values of the top 10,000 highly expressed genes in the EKLF/GFP+ F4/80+ RNA-Seq dataset.

Figure 5 with 2 supplements
EKLF specifies lineage and cell-cycle transcription factors in F4/80+ fetal liver (FL) island macrophages.

(A) Scatterplot of log2-fold changes in EKLF/GFP+ plotted against EKLF-/-. Red box shows the genes that are common and of interest from both datasets, that is, enriched in EKLF/GFP+ and downregulated in EKLF-/- F4/80+ FL macrophages (source data: Figure 5—source data 1). (B) Venn diagram showing the number of genes in each category from (A). Centrimo analysis of promoters of EKLF-dependent genes showing differential motif enrichment of (C) EKLF/Klf1 and (D) Klf3, Sp4, E2f1, and E2f4 motifs (source data: Figure 5—source data 2, 3). Dotted line depicts the expected probability of occurrence of the respective motif in the background dataset (see 'Materials and methods'). (E) Heatmap showing log2-fold change of expression in EKLF-/- and EKLF/GFP+ of the above EKLF-dependent transcription factors in F4/80+ FL macrophages.

Figure 5—source data 1

Expression values of differentially expressed genes in EKLF-/- cells vs WT cells compared with their expression in the EKLF/GFP+ dataset.

Figure 5—source data 2

FASTA sequences of the promoters of EKLF-dependent genes.

Figure 5—source data 3

FASTA sequences of the promoters of all genes not included in the EKLF-dependent gene set.

Figure 5—figure supplement 1
EKLF-dependent genes expressed in F4/80+ fetal liver (FL) macrophages.

(A) Heatmap showing log2-fold change expression in EKLF-/- over WT and EKLF/GFP+ over EKLF/GFP- of all the 504 EKLF-dependent genes in F4/80+ FL macrophages (source data: Figure 5—figure supplement 1—source data 1). One in every six genes’ name is displayed for visibility. (B) Centrimo analysis of E2f2 promoter of EKLF-dependent genes showing differential motif enrichment of E2f2 motifs (source data: Figure 5—source data 2, 3). Dotted line depicts the expected probability of occurrence of the E2f2 motif in the background dataset (see 'Materials and methods'). (C) Heatmap showing log2-fold change expression of potential EKLF-dependent transcription factors (source data: Figure 5—figure supplement 1—source data 1). Red boxes highlight the lineage Klf transcription factors and the cell-cycle E2f transcription factors.

Figure 5—figure supplement 1—source data 1

Expression values of EKLF-dependent genes.

Figure 5—figure supplement 2
EKLF-dependent signature genes in F4/80+ fetal liver (FL) macrophages.

(A) Heatmap of log2 FPKM values of F4/80+ FL signature genes (Figure 1E) that are also enriched in F4/80+ EKLF/GFP+ cells (source data: Figure 5—figure supplement 2—source data 1). (B) Heatmap of log2 FPKM and fold changes of FL signature genes that are significantly downregulated in EKLF-/- (source data: Figure 5—figure supplement 2—source data 2). Red boxes depict the EKLF-dependent signature genes that are common to both (A) and (B). (C) Density plot of flow cytometry analysis of E13.5 FLs stained with F4/80 and Adra2b antibodies. Gating scheme for F4/80-hi and Adra2b+ cells is shown in blue. The percentage of double-positive Adra2b+ and F4/80-hi cells, compared to total Adra2b+ cells, is indicated. (D) Representative pictures of erythroblastic islands from E13.5 FLs are shown after immunostaining with anti-F4/80 (red) or anti-Adra2b (green) antibodies. DNA stain was with DAPI (blue).

Figure 5—figure supplement 2—source data 1

Expression values of signature genes from Figure 1E that are significantly enriched in EKLF/GFP+ cells.

Figure 5—figure supplement 2—source data 2

Expression of signature genes from Figure 1E that are significantly downregulated in EKLF-/- cells compared to WT.

Figure 6 with 3 supplements
Resolving the cellular heterogeneity of E13.5 fetal liver (FL) macrophages using single-cell RNA-Seq.

(A) Unsupervised clustering using principal component analysis and subsequent U-MAP projections computed and plotted using the R Seurat package for single-cell RNA-Seq of purified E13.5 FL F4/80+ cells. Cluster numbers are indicated on the clusters. (B) Violin plot showing the distribution of F4/80 (Adgre1) mRNA expression in the clusters identified in (A). (C) Feature plots (left panel) showing individual cellular expression superimposed on the cluster, and Violin plots (right) showing the distribution of expression in each cluster of macrophage markers Vcam1 and Marco, and the macrophage-specific transcription factor PU.1 (Spic). (D) Differential mRNA enrichment in each cluster plotted as a heatmap, showing putative unique markers of each cluster (source data: Figure 6—source data 1). Relative expression levels are indicated by color: yellow=high, black=mid, and purple=low.

Figure 6—source data 1

Differentially expressed genes associated with each cluster of the single-cell RNA-Seq dataset.

Figure 6—figure supplement 1
F4/80 purity check.

F4/80-PE cells isolated from E13.5 fetal livers using the EZSep (Cell Signaling Technologies) magnetic bead method in the presence of Icam4/αv inhibitor peptide (Xue et al., 2014) and analyzed by flow cytometry to determine purity of the F4/80+ population for single-cell sequencing.

Figure 6—figure supplement 2
Markers for each gene expression-based cluster of cells identified from single-cell sequencing of F4/80+ fetal liver macrophages.

Cluster number and marker names are indicated.

Figure 6—figure supplement 3
Markers of F4/80+ cell clusters with various cell identities.

Violin plot showing the distribution (left) and feature plot showing individual cell expression (right) of (A) Csf1r, Dnase2a, and Il4rα genes associated with activated macrophages; (B) Gypa, Snca, and Spta1 genes, which are markers for erythro-myeloid characteristics; (C) Gapdh; and (D) Tfrc (CD71), which are uniformly expressed in most clusters.

EKLF/Klf1-expressing clusters in F4/80+ fetal liver macrophages.

Violin plots showing distribution (left) and feature plots (right) showing individual cellular mRNA expression of (A) Klf1, (B) Epor, and (C) Adra2b superimposed on the clusters.

Identification of novel markers for F4/80+/EKLF+ fetal liver macrophages from single-cell sequencing.

Using differential enrichment analysis of EKLF clusters 4, 5, and 7 compared with the rest of the cells, putative markers for F4/80+ EKLF+ cells were identified. (A) Violin and feature plots for the identified markers Add2 (adducinβ), Hemgn (hemogen), Nxpe2 (neurexophilin and PC-esterase domain family, member2), and Sptb (spectrinβ). (B) Data (as in Figure 2C, Mass et al., 2016) showing RNA-Seq reads of F4/80+ EKLF+ cell markers from staged and sorted fetal or postnatal liver macrophages. (C) FPKM expression levels of EKLF markers in F4/80+ EKLF/GFP+ and F4/80+ EKLF/GFP- fetal liver macrophage. (D) FPKM expression levels of EKLF markers Add2 and Hemgn in F4/80+ EKLF+/+ and F4/80+ EKLF-/- fetal liver macrophage.

An improved strategy for antibody-based isolation of F4/80+/EKLF+ cells using novel markers identified from single-cell sequencing.

(A) Flow cytometry analysis of E13.5 fetal liver cells stained with anti-F4/80-PE and anti-adducinβ (top) or anti-spectrinβ (below) antibodies conjugated to AlexaFluor 647. Gates are drawn based on unstained and single-color compensation controls for PE and AlexaFluor 647. Population percentages within each gate are indicated. (B) F4/80+ cells purified from E13.5 fetal livers using magnetic bead selection stained for anti-adducinβ (top) or anti-spectrinβ (below). Gates are the same as (A) and population percentages are indicated. (C) Imaging flow cytometry analysis of E13.5 fetal liver cells from the pEKLF/GFP mouse stained for F4/80-PE and Add2-TxRed. Single cells positive for F4/80, Add2, and GFP are shown. (D, E) Isolated erythroblast islands stained for DAPI, F4/80-PE, and (D) Sptb-Alexa647 or (E) Add2-Alexa647 and examined by fluorescent microscopy. Scale bars are indicated.


Table 1
Summary of significant GO terms for the subset of genes significantly downregulated in EKLF-/- vs WT.
Term_IDDescriptionFrequency (%)log10 p-value
GO:0006464Cellular protein modification process16.80−6.8427
GO:0032502Developmental process27.72−6.6535
GO:0048518Positive regulation of biological process24.84−4.1355
GO:0044699Single-organism process65.98−3.6956
GO:0022610Biological adhesion6.66−3.5792
GO:0016043Cellular component organization27.23−3.3699
GO:0008152Metabolic process51.22−3.3045
GO:0071840Cellular component organization or biogenesis27.98−3.2058
GO:0065007Biological regulation57.48−3.1341
GO:0065008Regulation of biological quality15.62−2.4455
GO:0044763Single-organism cellular process47.39−2.4259
GO:0007169Transmembrane receptor protein tyrosine kinase signaling pathway2.62−2.028
GO:0009791Post-embryonic development0.60−1.8994
GO:0098609Cell–cell adhesion4.30−1.7231
GO:0008219Cell death8.78−1.5167
GO:0002376Immune system process11.16−1.4235
GO:0009987Cellular process75.10−1.408
Table 2
Summary of GO terms for genes significantly enriched in EKLF/GFP+ F4/80+ fetal liver macrophages.
Term_IDDescriptionFrequency (%)log10 p-valueueUniqueness
GO:0006778Porphyrin-containing compound metabolic process0.18−12.84840.777
GO:0051186Cofactor metabolic process1.60−12.21110.915
GO:0033013Tetrapyrrole metabolic process0.20−11.15380.869
GO:0034101Erythrocyte homeostasis0.58−8.30270.786
GO:0051234Establishment of localization21.48−7.68310.957
GO:0065008Regulation of biological quality15.62−6.81150.959
GO:0055085Transmembrane transport5.98−6.49750.945
GO:0042592Homeostatic process7.64−6.18260.886
GO:0061515Myeloid cell development0.32−5.69970.83
GO:0048731System development21.00−5.44230.93
GO:1901564Organonitrogen compound metabolic process9.12−5.14380.923
GO:0042744Hydrogen peroxide catabolic process0.07−5.00010.868
GO:0006811Ion transport7.05−4.94420.946
GO:0048513Animal organ development15.85−4.73310.926
GO:0008152Metabolic process51.22−4.37560.997
GO:0044237Cellular metabolic process45.64−4.31620.937
GO:0055076Transition metal ion homeostasis0.58−4.29230.831
GO:0007275Multicellular organism development23.55−4.1610.933
GO:0032502Developmental process27.72−4.10780.994
GO:0048872Homeostasis of number of cells1.37−4.03860.845
GO:0008643Carbohydrate transport0.71−3.98260.911
GO:0048878Chemical homeostasis4.85−3.83720.85
GO:0006796Phosphate-containing compound metabolic process13.70−3.72120.925
GO:0006793Phosphorus metabolic process14.00−3.53640.925
GO:0055072Iron ion homeostasis0.38−3.50320.829
GO:0030099Myeloid cell differentiation1.70−3.39380.848
GO:0042440Pigment metabolic process0.30−3.20460.893
GO:0050801Ion homeostasis3.30−3.13720.843
GO:0048856Anatomical structure development25.70−2.95980.947
GO:0006820Anion transport2.43−2.84030.941
GO:0017001Antibiotic catabolic process0.52−2.69120.876
GO:0098771Inorganic ion homeostasis3.02−2.65730.839
GO:0019755One-carbon compound transport0.06−2.44160.9
GO:0019725Cellular homeostasis3.80−2.11480.81
GO:0071704Organic substance metabolic process49.01−2.00660.945
Table 3
Summary of GO terms for genes significantly enriched in EKLF/GFP- F4/80+ fetal liver macrophages.
Term_IDDescriptionFrequency (%)log10 p-valueueUniqueness
GO:0002376Immune system process11.16−90.87850.492
GO:0001775Cell activation4.73−58.59490.502
GO:0045321Leukocyte activation4.17−57.32760.435
GO:0001816Cytokine production2.93−56.18980.522
GO:0001817Regulation of cytokine production2.62−48.80350.477
GO:0006928Movement of cell or subcellular component7.93−47.60960.474
GO:0006954Inflammatory response2.89−45.49180.524
GO:0022610Biological adhesion6.66−45.40190.519
GO:0030334Regulation of cell migration3.21−37.86610.468
GO:0051707Response to other organism4.45−34.35090.498
GO:0009607Response to biotic stimulus4.67−34.27130.514
GO:0030155Regulation of cell adhesion2.92−34.14830.502
GO:0022603Regulation of anatomical structure morphogenesis4.20−33.77190.47
GO:0008283Cell proliferation8.83−29.25390.482
GO:0030036Actin cytoskeleton organization2.80−28.02140.516
GO:0030029Actin filament-based process3.14−27.38930.523
GO:0035295Tube development3.20−25.19160.507
GO:0008219Cell death8.78−24.58970.468
GO:0070661Leukocyte proliferation1.41−23.56230.563
GO:0072358Cardiovascular system development3.13−23.09940.498
GO:0006793Phosphorus metabolic process14.00−22.23850.449
GO:0044093Positive regulation of molecular function7.70−22.18550.47
GO:0098657Import into cell0.29−19.19130.625
GO:0050764Regulation of phagocytosis0.36−18.95050.578
GO:1902533Positive regulation of intracellular signal transduction4.07−18.41090.439
GO:0051704Multiorganism process6.53−17.90650.52
GO:0034097Response to cytokine3.34−17.03370.525
GO:0032940secretion by cell4.11−16.22490.486
GO:0002699Positive regulation of immune effector process0.86−14.24440.51
GO:0007167Enzyme-linked receptor protein signaling pathway4.02−14.19060.48
GO:0001774Microglial cell activation0.07−12.17130.623
GO:0010942Positive regulation of cell death2.72−11.16170.486
GO:0097435Supramolecular fiber organization2.73−11.11080.525
GO:0042592Homeostatic process7.64−10.95530.477
GO:0035456Response to interferon-beta0.22−10.23020.627
GO:0008360Regulation of cell shape0.65−10.14630.547
GO:0042107Cytokine metabolic process0.55−9.760.605
GO:0050777Negative regulation of immune response0.61−9.71840.523
GO:0002444Myeloid leukocyte-mediated immunity0.38−9.70990.588
GO:0090130Tissue migration1.14−9.66760.563
GO:0051129Negative regulation of cellular component organization2.94−9.02790.504
Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)Klf-/- (Klf1tm1Sho)10.1038/375318a0MGI:1857162EKLF-null mouse in 129S4/SvJae background
Genetic reagent (Mus musculus)pEKLF/GFP10.1242/dev.018200Peklf-GFPeGFP expressed from the EKLF promoter
AntibodyAnti-F4/80-PE (rabbit polyclonal)eBiosciences#12-4801-80(1:100)
AntibodyAnti-Adra2b (rabbit polyclonal)Alomone Labs#AAR-021(1:100)
(mouse monoclonal)
Santa Cruz Biotechnologies# sc-376063(1:100)
AntibodyAnti-spectrinβ1 (mouse monoclonal)Santa Cruz Biotechnologies# sc-374309(1:100)
AntibodyDonkey anti-rabbit IgG – AlexaFluor 647
(donkey polyclonal)
Invitrogen# A-31573(1:200)
Peptide, recombinant proteinFWV peptide (GenScript custom)10.1242/dev.103960# SC18482 mM
Commercial assay or kitEasySep mouse PE positive selection kitCell Signaling Technologies# 17656
Commercial assay or kitZip AlexaFluor 647 antibody labeling kitInvitrogen# Z11235
Commercial assay or kitLightning link Texas red conjugation kitAbcam# ab195225
Commercial assay or kitRNA Nanoprep kitAgilent#400753
Commercial assay or kitChromium Single Cell 3ʹ Library Kit v310X Genomics# PN-1000095
Chemical compoundTRIzol reagentInvitrogen#15596026
Software, algorithmSTAR10.1093/bioinformatics/bts635RRID:SCR_015899
Software, algorithmSalmon10.1038/nmeth.4197RRID:SCR_017036
Software, algorithmHTSeq10.1093/bioinformatics/btu638RRID:SCR_005514
Software, algorithmtximporthttps://github.com/mikelove/tximportRRID:SCR_016752
Software, algorithmDESeq210.1186/s13059-014-0550-8RRID:SCR_015687
Software, algorithmAlevin10.1186/s13059-019-1670-yhttps://salmon.readthedocs.io
Software, algorithmSeurathttps://doi.org/10.1038/nbt.4096http://satijalab.org/seurat/
Software, algorithmggplot2https://github.com/tidyverse/ggplot2RRID:SCR_014601
Software, algorithmPandashttps://pandas.pydata.orgRRID:SCR_018214
Software, algorithmScikit-learnhttp://scikit-learn.org/RRID:SCR_002577
Software, algorithmPython Seabornhttps://seaborn.pydata.org/RRID:SCR_018132
Software, algorithmJava Treeview10.1093/bioinformatics/bth349RRID:SCR_016916
Software, algorithmCluster 3.010.1093/bioinformatics/bth078RRID:SCR_013505
Software, algorithmREViGOhttp://revigo.irb.hr/RRID:SCR_005825
Software, algorithmGeneric GO Term Finder10.1093/bioinformatics/bth456RRID:SCR_008870
Software, algorithmMEME-suitehttp://meme-suite.org/RRID:SCR_001783
Software, algorithmFCS Express 7https://www.denovosoftware.comRRID:SCR_016431

Data availability

Data were deposited in GEO, accession number: GSE156153. Source data are included for Figures 1,3,4,5,6. R and Python code is deposited in https://github.com/mkaustav84/biekerlab-f480_macrophage; copy archived at https://archive.softwareheritage.org/swh:1:rev:907b15e74d998c5dd2a3106bce30af812c2b60b4/.

The following data sets were generated
    1. Mukherjee K
    2. Planutis A
    3. Xue L
    4. Bieker JJ
    (2021) NCBI Gene Expression Omnibus
    ID GSE156153. EKLF/Klf1 expression specifies a unique macrophage subset during mouse erythropoiesis.
The following previously published data sets were used
    1. Lavin Y
    2. Winter D
    3. Blecher-Gonen R
    4. David E
    5. Keren-Shaul H
    6. Merad M
    7. Jung S
    8. Amit I
    (2014) NCBI Gene Expression Omnibus
    ID GSE63340. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment.

Additional files

Supplementary file 1

Revigo analysis of the functions of fetal liver F4/80+ signature genes.

Supplementary file 2

DESeq2 results of significantly downregulated genes in EKLF-/- cells.

Supplementary file 3

DESeq2 results of significantly enriched genes in EKLF/GFP+ cells.

Supplementary file 4

Complete results of Centrimo analysis of the promoters of EKLF-dependent genes in F4/80+ macrophages.

Supplementary file 5

GO analysis of the top 100 differentially enriched genes in clusters 0, 1, 2, and 3 of the single-cell sequencing data.

Supplementary file 6

GO analysis of the top 100 differentially enriched genes in clusters 4, 5, 7, and 8 of the single-cell sequencing data.

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