Literature DB >> 35904250

CXCR4^high megakaryocytes regulate host-defense immunity against bacterial pathogens.

Jin Wang¹, Jiayi Xie^2,3, Daosong Wang³, Xue Han^2,3, Minqi Chen³, Guojun Shi¹, Linjia Jiang², Meng Zhao^2,3.

Abstract

Megakaryocytes (MKs) continuously produce platelets to support hemostasis and form a niche for hematopoietic stem cell maintenance in the bone marrow. MKs are also involved in inflammatory responses; however, the mechanism remains poorly understood. Using single-cell sequencing, we identified a CXCR4 highly expressed MK subpopulation, which exhibited both MK-specific and immune characteristics. CXCR4high MKs interacted with myeloid cells to promote their migration and stimulate the bacterial phagocytosis of macrophages and neutrophils by producing TNFα and IL-6. CXCR4high MKs were also capable of phagocytosis, processing, and presenting antigens to activate T cells. Furthermore, CXCR4high MKs also egressed circulation and infiltrated into the spleen, liver, and lung upon bacterial infection. Ablation of MKs suppressed the innate immune response and T cell activation to impair the anti-bacterial effects in mice under the Listeria monocytogenes challenge. Using hematopoietic stem/progenitor cell lineage-tracing mouse lines, we show that CXCR4high MKs were generated from infection-induced emergency megakaryopoiesis in response to bacterial infection. Overall, we identify the CXCR4high MKs, which regulate host-defense immune response against bacterial infection.

Entities: Chemical

Keywords: cell biology; hematopoietic stem cell; host-defense immunity; megakaryocytes; megakaryopoiesis; mouse

Mesh：

Year: 2022 PMID： 35904250 PMCID： PMC9374440 DOI： 10.7554/eLife.78662

Source DB: PubMed Journal: Elife ISSN： 2050-084X Impact factor: 8.713

Introduction

Megakaryocytes (MKs) are large and rare hematopoietic cells in the bone marrow, which continually produce platelets to support hemostasis and thrombosis (Deutsch and Tomer, 2006). MK progenitors undergo multiple rounds of endomitosis during maturation to achieve polyploidy (Chang et al., 2007; Deutsch and Tomer, 2013; Machlus and Italiano, 2013; Nagata et al., 1997; Patel et al., 2005). MKs and their progenitors migrate between distinct microenvironments and organs for their proliferation, maturation, and biological functions (Avecilla et al., 2004; Fuentes et al., 2010; Lefrançais et al., 2017; Pal et al., 2020; Tamura et al., 2016; Wang et al., 1998). Although platelet generation is the prominent role of MKs, emerging evidence suggests that MKs have other biological functions. Mature MKs interact with HSCs and constitute a unique niche to preserve HSC quiescence in the bone marrow (Bruns et al., 2014; Zhao et al., 2014). MKs also interact with other niche cells, such as osteoblasts (Dominici et al., 2009; Olson et al., 2013), non-myelinating Schwann cells (Jiang et al., 2018; Yamazaki et al., 2011), and blood vessels (Avecilla et al., 2004; Saçma et al., 2019) to further influence the attraction and retention of hematopoietic stem and progenitor cells during homeostasis and stress. MK-biased hematopoietic stem cells (HSCs) induce emergency megakaryopoiesis to actively generate MKs upon acute inflammation, which can efficiently replenish the platelet loss during inflammatory insult (Haas et al., 2015). Studies suggested that MKs might participate in immune responses independent of their platelet generation role (Cunin and Nigrovic, 2019). MKs express multiple immune receptors, such as IgG Fc receptors and toll-like receptors (TLRs), enabling them to sense inflammation directly (Cunin and Nigrovic, 2019). Mature MKs also express major histocompatibility complex (MHC) to activate antigen-specific CD8+ T cells and enhance CD4+ T cells and Th17 cell responses through stimulating antigen processing (Finkielsztein et al., 2015; Pariser et al., 2021; Zufferey et al., 2017). Furthermore, MKs release multiple cytokines and chemokines to influence immune cells. For example, MKs produce IL-1α and IL-1β to promote arthritis susceptibility in mice resistant to arthritis (Cunin et al., 2017) and produce CXCL1 and CXCL2 to promote neutrophil efflux from the bone marrow (Köhler et al., 2011). Lung MKs contribute to thrombosis (Lefrançais et al., 2017) and, more interestingly, participate in immune responses (Pariser et al., 2021), although the relationship between lung MKs and bone marrow circulating MKs (Nishimura et al., 2015) remains unexplored. Furthermore, the recent single-cell atlas shows that MKs are heterogeneous and contain subpopulations that express multiple immune genes and are involved in inflammation response (Liu et al., 2021; Pariser et al., 2021; Sun et al., 2021; Yeung et al., 2020). Here, by combining scRNA-seq with functional assays, we identified a CXCR4high MK population, which was generated by infection-induced emergency megakaryopoiesis, and stimulated innate immunity against bacterial infection.

Results

Single-cell atlas identifies an immune-modulatory subpopulation of MKs

We applied droplet-based scRNA-seq with CD41+ forward scatter (FSC)high bone marrow MKs to explore the MK heterogeneity (Figure 1A; Figure 1—figure supplement 1A,B). To enrich accurate MKs, we further performed transcriptomic profile analysis in the phenotypically enriched MKs (Yeung et al., 2020). Our scRNA-seq successfully detected 5368 high-quality cells (Figure 1—figure supplement 1B,C), in which one MK cluster (1712 cells) and six immune cell clusters (3656 cells) were annotated according to their gene profile (Figure 1—figure supplement 1D-G) and the alignment with published scRNA-seq data (Almanzar et al., 2020; Hamey et al., 2021; Pariser et al., 2021; Xie et al., 2020; Yeung et al., 2020). Our annotated MKs were similar to MKs but distinct to immune cells, including myeloid progenitors, basophils, neutrophils, monocytes, dendritic cells, macrophages, B cells, and T cells, in an integrated scRNA-seq analysis platform (Figure 1—figure supplement 2). Therefore, we re-clustered the transcriptionally enriched 1712 MKs into five subpopulations, termed MK1 to MK5 (Figure 1B; Figure 1—figure supplement 3A,B), which were further confirmed by the integrated scRNA-seq analysis platform to rule out the potential immune cell contamination (Pariser et al., 2021; Xie et al., 2020; Yeung et al., 2020; Figure 1—figure supplement 3C,D). We noticed that mature MKs with huge sizes were captured at a relatively low rate, potentially due to the limitation in current techniques in cell purification and single-cell preparation (Liu et al., 2021; Sun et al., 2021).

Figure 1.

Single-cell atlas identifies an immune-modulatory subpopulation of MKs.

(A) Schematic strategy for MK preparation, scRNA-seq and data analysis. (B) Clustering of 1712 bone marrow MKs. (C) Heatmap of signature gene expression in MK subpopulations (fold-change >1.5, p value <0.05) with exemplar genes listed on the right (top, color-coded by subpopulations). Columns denote cells; rows denote genes. Z score, row-scaled expression of the signature genes in each subpopulation. (D) Gene Ontology (GO) analysis of signature genes (fold-change >1.5, p value <0.05) for each MK subpopulations. GO terms selected with Benjamini–Hochberg-corrected p values <0.05 and colored by –log10(p value). Bubble size indicates the enriched gene number of each term. (E–F) UMAP visualization (E) and statistical analysis (F) of cytokine score (left), inflammatory score (middle) and chemokine score (right) in MK1 to 5. (G) Dotplots of significant cytokine ligand (source) -receptor (target) interactions between MKs and immune cells discovered. The color indicates the means of the receptor-ligand pairs between two cell types and bubble size indicates p values. Mon, monocytes; MΦ, macrophages (Dong et al., 2020); DC, dendritic cells; Neu, neutrophils; MP, myeloid progenitors; T, T cells; B, B cells. (H–I) Violin plot (H) and feature plot (I) of selected signature genes of MK5. Red lines in (H) indicate the median gene expression. Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (F), ǂǂ <0.01, ǂǂǂ p<0.001.

(A) Flow cytometry gating for isolation of MKs (CD41+ FSChigh) in bone marrow for scRNA-seq. (B) Quality control of scRNA-seq data. Violin plots showing the number of unique genes (gene number) before and after removing doublets, number of total unique molecular identifiers (UMI counts) and percentage of mitochondrial transcripts in single cells after removing doublets. Scatter plot showing the correlation between UMI counts and gene numbers. corr indicates Pearson correlation coefficient. (C–D) UMAP of 5368 bone marrow cells, colored by gene numbers (C) and by cluster identity indicated on the right (D). MK, megakaryocytes; Neu, neutrophils; MΦ, macrophages; MP, myeloid progenitors; DC, dendritic cells; Mon, monocytes; B, B cells; T, T cells. (E) Heatmap of row-scaled signature gene expression in each cell type (top, color-coded by subpopulations). Columns denote cells; rows denote genes. Z score, row-scaled expression of the signature genes in each subpopulation. (F) Feature plots showing selected gene expression. (G) Violin plots showing the number of unique genes (gene number) of 1712 MKs.

(A) Radar charts showing cell similarities of our single cell dataset with the published bone marrow MK single cells (Pariser et al., 2021; Yeung et al., 2020), bone marrow immune cells and myeloid progenitor single cells (Almanzar et al., 2020; Hamey et al., 2021; Xie et al., 2020) using MetaNeighbor R package. (B) Comparison of our bone marrow scRNA-seq data with published bone marrow MK (Pariser et al., 2021; Yeung et al., 2020) and immune cell (Almanzar et al., 2020; Hamey et al., 2021; Xie et al., 2020) datasets. All cells were integrated by iMAP.py and projected on UMAP. (C) Column-scaled Euclidean distances between the centroid of each cluster. Z score, column-scaled Euclidean distance. MK, megakaryocytes; Neu, neutrophils; MΦ, macrophages; MP, myeloid progenitors; DC, dendritic cells; Mon, monocytes; B, B cells; T, T cells.

(A) Re-clustering of 1712 MKs from 5368 cells. (B) Violin plots showing MK marker gene expression (Aburima et al., 2021; Bernardes et al., 2020; Kanaji et al., 2005; Liu et al., 2021; Couldwell and Machlus, 2019; Ozaki et al., 2005; Sun et al., 2021; Yeung et al., 2020) of MK1 to 5. Red lines indicate the median gene expression. (C) Projection of our datasets on reported MK (Pariser et al., 2021; Yeung et al., 2020) and immune cell (Hamey et al., 2021; Xie et al., 2020) scRNA-seq datasets by Symphony R package. MK, megakaryocytes; Neu, neutrophils; MΦ, macrophages; MP, myeloid progenitors; DC, dendritic cells; Mon, monocytes; Bas, basophils; B, B cells; T, T cells; ABM, adult bone marrow. (D) MKs (MK1 to 5) and cell types projection based on similarities of our single-cell transcriptional profiles and published MK (Pariser et al., 2021; Yeung et al., 2020) and immune cell datasets (Hamey et al., 2021; Xie et al., 2020) by scmap R package.

(A) Volcano plot showing MK5 and MK1 to 4 enriched genes (|fold change|>1.4, p value <0.05). (B–C) Gene Ontology (GO) analysis showing MK5 enriched immune pathway genes and MK1 to 4 mainly enriched hemopoiesis and RNA processing pathway genes. (D–F) Gene set enrichment analysis (GSEA) evaluated enrichment of selected pathways in MK5 cells compared to other MK subpopulations (MK1 to 4).

(A) Dotplots of significant cytokine ligand (source) -receptor (target) interactions between MKs and immune cells discovered using CellChat. Color indicates communication probabilities, and bubble size indicates p value of the ligand-receptor pairs between MK subpopulations (source) and immune cells (target). (B) Selected signature gene expression in MK subpopulations.

Figure 1—figure supplement 1.

Cell isolation, quality control and annotation of scRNA-seq data.

Figure 1—figure supplement 2.

Cell type identification by alignment with published scRNA-seq data.

Figure 1—figure supplement 3.

Identification of MK subpopulations.

Single-cell atlas identifies an immune-modulatory subpopulation of MKs.

Cell isolation, quality control and annotation of scRNA-seq data.

Cell type identification by alignment with published scRNA-seq data.

Identification of MK subpopulations.

Enriched genes in MK1 to 4 and MK5.

MK5 interacts with immune cells and express signature genes of immune MKs.

Polyploidy, platelet generation ability and cell size of CXCR4low and CXCR4high MKs.

(A) Representative flow cytometry plots of gating strategy of CD41 and CXCR4 in the bone marrow of control mice and mice 3 days after L. monocytogenes infection. (B) Relative expression of Pf4, Vwf and Mpl in CXCR4high and CXCR4low MKs by RT-qPCR. (C) Representative immunofluorescent staining image showing membranous CD41 staining typical of sorted bone marrow CXCR4high MKs. (D) Sorted CXCR4low and CXCR4high MKs produced proplatelets in vitro on day five post cultured under 100 ng ml–1 TPO. White arrowheads indicate proplatelet formation. (E) Polyploidy distribution of CXCR4low MKs and CXCR4high MKs (right). (F) Representative immunofluorescent staining images showing CD41 and CXCR4 expression of CXCR4low and CXCR4high MKs in the bone marrow. CD41, green; CXCR4, red; DAPI, blue. (G) Comparison of cell size between CXCR4low MKs and CXCR4high MKs on day three post cultured under 100 ng ml–1 Thrombopoietin (TPO). (H) Median fluorescence intensity of CXCR4 in small and large sizes of MKs by flow cytometry. Scale bars, 20µm. Data represent mean ± s.e.m in (B, D, E, H) or mean ±first and third quartiles in (G). Two-sample KS test was performed to assess statistical significance in (G). Two-tailed Student’s t-test was performed to assess statistical significance in (B, D, E, H). * p<0.05, ** p<0.01, *** p<0.001. Enriched signature genes by Gene Ontology exhibited that MK1 and MK2 highly expressed nuclear division, DNA replication and repair genes for endomitosis (Figure 1C,D). MK3 enriched blood coagulation and thrombosis genes for platelet generation (Figure 1C,D). No signature pathways were enriched in MK4. MK5 enriched cell migration and immune response genes (Figure 1C,D; Figure 1—figure supplement 4A-E), cytokine, chemokine (Figure 1E,F; Figure 1—figure supplement 4F), and genes involved in immune cell interaction (Figure 1G, Figure 1—figure supplement 5A). MK5 also expressed signature genes in recently reported inflammatory-related MKs (Cd53, Lsp1, Anxa1, Spi) (Sun et al., 2021) and immune MKs (Ccl3, Cd52, Selplg, Sell, Adam8) (Liu et al., 2021; Figure 1—figure supplement 5B). We also noticed that MK5 highly expressed Cxcr4 than other MK subpopulations (Figure 1H,I), although most MKs express CXCR4 (Hamada et al., 1998; Figure 1—figure supplement 6A). To confirm this, we found that CXCR4high MKs expressed MK markers (Figure 1—figure supplement 6B), were mainly polyploid cells (Figure 1—figure supplement 6C), and had platelet generation ability (Figure 1—figure supplement 6D), although they have relatively low polyploidy (Figure 1—figure supplement 6E) and smaller cell size (Figure 1—figure supplement 6F-H). CXCR4high MKs generated platelets in lower efficiencies compared to CXCR4low MKs (Figure 1—figure supplement 6D), suggesting CXCR4high MKs might be specialized for immune functions. Overall, using scRNA-seq, we identified an MK subpopulation that exhibited both MK-specific and immune transcriptional characteristics.

Figure 1—figure supplement 4.

Enriched genes in MK1 to 4 and MK5.

Figure 1—figure supplement 5.

MK5 interacts with immune cells and express signature genes of immune MKs.

Figure 1—figure supplement 6.

Polyploidy, platelet generation ability and cell size of CXCR4low and CXCR4high MKs.

CXCR4high MKs enhance myeloid cell mobility and bacterial phagocytosis

As MK5 enriched genes involved in myeloid cell activation (Figure 1—figure supplement 4E) and myeloid cell interactions (Figure 1G, Figure 1—figure supplement 5A), we further explored the role of CXCR4high MKs, which enriched MK5, in regulating myeloid immune cells, in regulating the innate immunity function of myeloid cells against pathogens. We challenged mice with Listeria (L.) monocytogenes, a Gram-positive facultative intracellular bacterium (Bishop and Hinrichs, 1987; Edelson and Unanue, 2000), which induce myelopoiesis (Eash et al., 2009; Figure 2—figure supplement 1A, B). Interestingly, we noticed that CXCR4high MKs were more dramatically associated with myeloid cells in the bone marrow of mice 3 days after L. monocytogenes infection, which was a significant increase than the association between myeloid cells and CXCR4low MKs or the association between randomly placed myeloid cells and CXCR4high MKs (Figure 2A,B). The myeloid cell-CXCR4high MK association (mean distance 15.36 μm) was significantly closer than the myeloid cell-CXCR4low MKs association (Figure 2C; mean distance 25.62 μm, p=7.0 × 10–4 by KS test), and the association between randomly placed myeloid cells and CXCR4high MKs [35.37 μm, p (μ<15.36)=1.8 × 10–10] in the bone marrow of mice 3 days after L. monocytogenes infection. Whereas the observed mean distance of myeloid cells to CXCR4low MKs (25.62 μm) is not different from random simulations [27.76 μm, p (μ<25.62)=0.14] (Figure 2C). This suggested that the increased association between myeloid cell-CXCR4high MK may not be due to the infection-induced expansion of myeloid cells. Furthermore, we did not observe a significant association between myeloid cells and MKs during homeostasis (Figure 2—figure supplement 1C,D). We also noticed that bone marrow myeloid cells were preferably adjacent to the CXCR4high MK-blood vessel intersection in mice 3 days after L. monocytogenes infection (Figure 2—figure supplement 1E,F). These observations indicated that CXCR4high MKs might regulate myeloid cells upon bacterial infection.

Figure 2—figure supplement 1.

L. monocytogenes promote myelopoiesis and the association of myeloid cells and the CXCR4high MK-blood vessel intersection.

(A–B) Representative immunofluorescent staining image (A) and statistical analysis (B) showing association of MKs (blue) and myeloid cells (green) in the bone marrow of control mice and mice 3 days after L. monocytogenes infection (n=30 control and infected MKs). CD41, blue; CD11b, green. (C–D) Distance (C) and mean distance (D) of actual or randomly positioned myeloid cells to the closest CXCR4low Mks and CXR4high MKs from mice without infection. (E–F) Representative images of MKs (blue), CXCR4 (red), vascular endothelial cells (green) and myeloid cells (white) (E), and statistical analysis (F) in bone marrow showing the distribution of myeloid cells to CXCR4low or CXCR4high MKs and vascular cells 3 days after L. monocytogenes infection. CD41, blue; CXCR4, red; EMCN, green; CD11b, white. Yellow arrows indicate CXCR4high MKs and white arrowheads indicate CXCR4low MKs (left) and two-dimensional probability distribution of distances from myeloid cells to CXCR4low or CXCR4high MKs and vascular cells (n=104 CD11b+ cells; p=0.04 by 2D KS test). Scale bars, 20µm. Boxplots show medians, first and third quartiles in (B). Two-sample KS test was performed to assess statistical significance in (B, D). Two-dimensional-two-sample KS test was performed to assess statistical significance in (F).

Figure 2.

CXCR4high MKs enhance myeloid cell mobility and bacteria phagocytosis.

(A) Distribution of myeloid cells to CXCR4low or CXCR4high MKs 3 days after L. monocytogenes infection. Representative images of MKs (blue), CXCR4 (red), and myeloid cells (green) in mouse bone marrow. Yellow arrows indicate CXCR4high MKs and white arrowheads indicate CXCR4low MKs. (B–C) Distance (B) and mean distance (C) of actual or randomly positioned myeloid cells to the closest CXCR4low and CXR4high MKs 3 days after L. monocytogenes infection. (D) Numbers of transmigrated myeloid cells normalized to Ctrl (without MKs in the lower chambers) as indicated by transwell assays. Mon, monocytes; MΦ, macrophages; DC, dendritic cells; Neu, neutrophils. (E–F) Representative images (E) and quantification (F) of neutrophil phagocytosis capacity with or without MK co-culture as indicated. CD11b, red; E. coli, green; DAPI, blue. Ctrl, neutrophil without MK co-culture. (G–H) Representative images (G) and quantification (H) of macrophage phagocytosis capacity with or without MK co-culture as indicated. F4/80, red; E. coli, green; DAPI, blue. Ctrl, macrophage without MK co-culture. (I) Spearman correlation analysis between expression profiles of Cxcr4 and feature genes in MK subpopulations. (J) Quantification of TNFα+ and IL-6+ cells in CXCR4low MKs and CXCR4high MKs from control mice or mice 3 days after L. monocytogenes infection. (K–L) Quantification of neutrophil (K) and macrophage (L) phagocytosis with or without CXCR4high MK co-culture in the absence or presence of anti-TNFα or anti-IL-6 neutralizing antibodies. (M–N) Quantification of the phagocytosis abilities by neutrophils (M) and macrophages (N) with or without CXCR4low MK or CXCR4high MK co-culture in the absence or presence of anti-TNFα or anti-IL-6 neutralizing antibodies by flow cytometry. Ctrl, neutrophils (M) or macrophages (N) without MKs co-culture. Scale bars, 20 μm (A, E, G). Data represent mean ± s.e.m (D) and boxplots show medians, first and third quartiles (F, H, K–L). Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (D, M, N), ǂ p<0.05, ǂǂ p<0.01, n.s., not significant. A two-sample KS test was performed to assess statistically significant (C, F, H, K, L), n.s., not significant. Paired Student’s t-test was performed to assess statistical significance (J), # p<0.05, ## p<0.01, n.s., not significant.

CXCR4high MKs enhance myeloid cell mobility and bacteria phagocytosis.

L. monocytogenes promote myelopoiesis and the association of myeloid cells and the CXCR4high MK-blood vessel intersection.

CXCR4high MKs promote myeloid cell phagocytosis and produce TNFα and IL-6.

(A–B) Quantification of phagocytosis abilities in neutrophils (A) and macrophages (B) without or with CXCR4low MKs or CXCR4high MKs as indicated. (C–E) TNFα and IL-6 protein levels in CXCR4low and CXCR4high MKs were shown by immunofluorescent staining (C–D) using sorted MKs, compared to their levels in sorted macrophages upon L. monocytogenes infection (E).Scale bars, 5μm. Data represent mean ± s.e.m in (A, B) and boxplots show medians, first and third quartiles in (C–E). Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (A, B), ǂ p<0.05, ǂǂ p<0.01. Two-sample KS test was performed to assess statistical significance in (C–E). To explore how CXCR4high MKs regulate myeloid cells, we interestingly found that CXCR4high MKs, but not CXCR4low MKs, effectively promoted myeloid cell mobilization in our transwell assays (Figure 2D). Furthermore, we asked whether CXCR4high MKs regulate myeloid cell function against pathogens. To this aim, we incubated purified CXCR4low MKs and CXCR4high MKs with neutrophils or macrophages for bacterial phagocytosis analysis. We found that CXCR4high MKs, but not CXCR4low MKs, efficiently enhanced the bacterial phagocytosis of neutrophils and macrophages (Figure 2E–H; Figure 2—figure supplement 2A,B).

Figure 2—figure supplement 2.

CXCR4high MKs promote myeloid cell phagocytosis and produce TNFα and IL-6.

Our scRNA-seq also exhibited that the high expression of Cxcr4 was positively correlated with immune cell-stimulating cytokines, such as Ccl6, Tnf, and Il6 (Li et al., 2018; Rothe et al., 1993; Shapouri-Moghaddam et al., 2018) in MKs (Figure 2I). In line with this, CXCR4high MKs had higher TNFα and IL-6 protein levels than CXCR4low MKs (Figure 2J; Figure 2—figure supplement 2C,D). The TNFα and IL-6 levels in CXCR4high MKs were comparable to macrophages from mice 3 days after L. monocytogenes infection (Figure 2—figure supplement 2E), which are known as the primary cellular source of TNFα and IL-6 upon infection (Shapouri-Moghaddam et al., 2018). These observations suggested that CXCR4high MKs might stimulate myeloid cell phagocytosis by producing TNFα and IL-6. Indeed, anti-TNFα and anti-IL-6 blocking antibodies compromised the role of CXCR4high MKs in stimulating bacterial phagocytosis of neutrophils and macrophages (Figure 2K–N).

CXCR4high MKs stimulate host-defense immunity against bacterial pathogens

To explore the in vivo role of MKs upon L. monocytogenes infection in mice, we employed Pf4 mice, in which MKs were rendered sensitive to diphtheria toxin (DT) (Zhao et al., 2014; Figure 3A and B). MK ablation increased the number of hematopoietic stem and progenitor cells and myelopoiesis in the bone marrow upon infection (Figure 3—figure supplement 1A-D). Notably, MK ablation dramatically increased the bacterial burdens in the liver and spleen 3 days after L. monocytogenes infection (Figure 3C). We also found that MK ablation reduced the number of myeloid cells, including monocytes, macrophages, dendritic cells (DCs), and neutrophils, in the liver and spleen (Figure 3D,E; Figure 3—figure supplement 1E,F), suggesting the role of MKs in promoting myeloid cells against pathogens. We further investigated whether MKs regulate adaptative immunity against pathogen infection. Interestingly, we noticed that CXCR4high MKs were able to phagocytose bacteria and presented the ovalbumin (OVA) antigens on their surface via MHC-I (Figure 3F,G). Furthermore, OVA antigens presented by CXCR4high MKs activated OT-I CD8+ T cells (Figure 3H) and B3Z T cells (Figure 3—figure supplement 2), a T cell hybridoma which expresses TCR that specifically recognizes OVA (Karttunen et al., 1992). We challenged Pf4 mice with OVA-expressing recombinant microbe (L. monocytogenes-OVA). Seven days after L. monocytogenes-OVA infection, splenocytes from control or MK ablated mice were re-stimulated with OVA peptide in vitro to assess OVA-specific T cell activation (Figure 3I). Notably, MK ablation dramatically reduced the number of CD4+ IFNγ+ Th1, CD4+ IL4+ Th2, and CD8+ cytotoxic T lymphocytes but did not impact the total number of CD4+ T cells and CD8+ T cells (Figure 3J). These observations demonstrated that MKs regulate host-defense immunity against L. monocytogenes infection. To explore whether CXCR4high MKs contribute to the immune response against bacterial pathogens, we infused the purified CXCR4high MKs and CXCR4low MKs into MK ablation mice during L. monocytogenes infection (Figure 3K). Notably, we found that the infusion with CXCR4high MKs, but not CXCR4low MKs, partially rescued the bacterial clearance defect in MK ablation mice (Figure 3L). This is potentially due to the reduced platelets known for regulating immune responses (Semple et al., 2011).

Figure 3.

CXCR4high MKs stimulate host-defense immunity against bacterial pathogens.

(A) Schema for diphtheria toxin (DT) and L. monocytogenes administration used for the experiments shown in (B–E). (B) Representative images of MKs (blue, indicated by arrows) and vascular endothelial cells (green) in the bone marrow of mice after four daily DT treatments. (C) Bacterial burdens in the liver and spleen of Pf4 mice 3 days after L. monocytogenes (L.m.) infection with four-time DT injections. (D–E) Myeloid cells in the liver (D) and spleen (E) of Pf4 mice 3 days after L. monocytogenes (L.m.) infection with four-time DT injections. (F) Quantification of bacterial phagocytosis capacities of neutrophils, CXCR4low MKs and CXCR4high MKs. (G) Quantification of MHCI-OVA levels on CXCR4low MKs, CXCR4high MKs and bone-marrow-derived dendritic cells (DCs) upon a pulse of 24 hr with or without OVA. (H) Quantification of activated OT-I CD8+ T cells after co-culture with bone-marrow-derived DCs, OVA-pulsed bone-marrow-derived DCs, CXCR4low MKs, or CXCR4high MKs. (I) Schema for antigen-specific T cell activation assay shown in (J). (J) Splenocytes from control or MK ablated mice 7 days after L. monocytogenes-OVA257-264 infection and seven DT injections were stimulated with OVA peptide in vitro for 4 hr, and antigen-specific activated T cells were quantified (n=4 mice). L.m.-OVA, L. monocytogenes-OVA257-264. (K) Schema for DT, L. monocytogenes administration, MK transfusing and bacterial burden determination shown in (L). (L) Bacterial burdens in the liver and spleen of Pf4 mice without or with CXCR4low or CXCR4high MK transfused. Scale bars, 20μm. Data represent mean ± s.e.m. Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (F), ǂǂǂ p<0.001. Paired Student’s t-test was performed to assess statistical significance (G), # p<0.05, n.s., not significant. Two-tailed Student’s t-test was performed to assess statistical significance except (F), * p<0.05, ** p<0.01, *** p<0.001, n.s., not significant.

(A) Schema for diphtheria toxin (DT) and L. monocytogenes administration used for the experiments shown in (B–D). (B) Number of bone marrow hematopoietic stem and progenitor cells in Pf4+/- mice compared to Pf4-; Rosa26+/- mice after L. monocytogenes infection and DT injection. (C–D) Gating strategies (C) and numbers (D) of myeloid cells in the bone marrow of Pf4+/- mice after L. monocytogenes infection and DT injection. (D–E) Gating strategies of myeloid cells in the liver (D) and spleen (E) of Pf4+/- mice at 3 days after L. monocytogenes infection. Neu, neutrophil; MF, macrophage; Mono, monocyte; DC, dendritic cell. Two-tailed Student’s t-test was performed to assess statistical significance. * p<0.05, ** p<0.01, *** p<0.001.

The LacZ activity in B3Z T cells with or without co-culture as indicated. Ctrl indicates B3Z cells without MKs co-culture; bone-marrow-derived DCs, CXCR4low MKs and CXCR4high MKs were pulsed with OVA for 24 hr. Two-tailed Student’s t-test was performed to assess statistical significance, * p<0.05.

Figure 3—figure supplement 1.

MK ablation influenced HSC and myeloid cell numbers.

Figure 3—figure supplement 2.

CXCR4high MKs induced B3Z T cell activation.

CXCR4high MKs stimulate host-defense immunity against bacterial pathogens.

MK ablation influenced HSC and myeloid cell numbers.

CXCR4high MKs induced B3Z T cell activation.

Bacterial infection stimulates the migration of CXCR4high MKs

High Cxcr4 expression indicated that CXCR4high MKs might migrate between the bone marrow microenvironment and circulation in response to infection (Suraneni et al., 2018). In line with this, our spatial distribution analysis showed that ~80% of MKs directly contacted blood vessels 3 days after L. monocytogenes infection, which was much higher than in control mice (~40%) (Figure 4A,B; Figure 4—figure supplement 1A). Furthermore, more CXCR4high MKs, with small cell sizes (Figure 1—figure supplement 6F-H), were tightly associated with blood vessels and trapped in the sinusoid than CXCR4low MKs 3 days after L. monocytogenes infection (Figure 4C,D). However, L. monocytogenes infection did not influence the association between MKs and HSCs (Figure 4A,E), albeit the critical role of perivascular MKs in maintaining HSC quiescence (Bruns et al., 2014; Itkin et al., 2016; Zhao et al., 2014) and the dramatic HSC activation upon infection (Figure 4—figure supplement 1B).

Figure 4.

Bacterial infection stimulates the migration of CXCR4high MKs.

(A) Representative image of CD41 (blue), CD150 (red), EMCN (green), and lineage cells (white) in bone marrow from control mice or mice at 3 days after L. monocytogenes infection. dHSC and dEC indicate the distance between the MK (blue, marked with an asterisk) and the closest HSC (red), endothelial cell (green), respectively. Yellow boxes indicate the locations of the magnified images. Arrowheads indicate HSCs. EMCN, endomucin; EC, endothelial cell. (B) Comparison of the distance between MKs to Ecs (n=119 control and 103 infected MKs) in the bone marrow of control mice or mice at 3 days after L. monocytogenes infection. (C) Comparison of the distance between CXCR4low or CXCR4high MKs and endothelial cells (Ecs) in the bone marrow of mice 3 days after L. monocytogenes infection (n=68 CXCR4low and 78 CXCR4high MKs). CD41 (blue), CXCR4 (red), and EMCN (green). Yellow arrows indicate CXCR4high MKs, while white arrowheads indicate CXCR4low MKs. (D) Representative immunofluorescent staining images (left) and quantification (right) of CXCR4 (red) labeled MKs (blue) egressed into sinusoids (green) upon infection (n=46 CXCR4low MKs and 69 CXCR4high MKs in 4 biological replicates). Yellow arrows indicate CXCR4high MKs. (E) Comparison of the distance between HSCs to MKs (n=96 control and 127 infected HSCs, p=0.21 by two-sample KS test) in the bone marrow of control mice or mice at 3 days after L. monocytogenes infection. (F) Visualization of MK migration (red, arrowhead) into sinusoids (green) by live imaging in the bone marrow of Pf4+; Rosa26+/- mice 24 hr after L. monocytogenes infection (Movie S1). ‘S’ indicates sinusoid and dashed lines demarcate the border of sinusoids. (G) Quantification of CXCR4high MKs in bone marrow, peripheral blood, liver, spleen, and lung of control mice and mice 3 days after L. monocytogenes infection. (H) Quantification of CXCR4 levels on CXCR4low MKs treated with IFN-γ, LPS or L. monocytogenes for 4 hr compared to CXCR4high MKs. (I) Quantification of tdTomato+ CXCR4high MKs in the liver and spleen from control mice or mice 3 days after L. monocytogenes infection by flow cytometry. (J) Schema of mtdTomato+ bone marrow (from R26R mice) cell perfusion in control and L. monocytogenes infected recipients. (K) The percentage of CXCR4high mtdTomato+ MKs and CXCR4low mtdTomato+ MKs in bone marrow, liver, and spleen of control or infected recipients were analyzed 2 days after mtdTomato+ bone marrow cells were perfused. (L) Radar chart showing transcriptomic similarities of bone marrow MK subpopulations with reported BM and lung MK datasets (Pariser et al., 2021; Yeung et al., 2020). (M) Schema for transfer experiments using tdTomato+ MKs from Pf4+; Rosa26+/- mice into control recipients or recipients 1 day following L. monocytogenes infection. (N–O) Representative images (N) and quantification by flow cytometry (O) of tdTomato+ MKs in the lung of control or infected recipients 2 days after cell perfusion (n=3 mice). Arrows indicate tdTomato+ MKs in the lung. Scale bars without indicated, 20 μm. Data represent mean ± s.e.m. A two-sample KS test was performed to assess statistical significance in (E). Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (H), ǂǂ p<0.01, ǂǂǂ p<0.001. Two-tailed Student’s t-test was performed to assess statistical significance except (E, H), * p<0.05, ** p<0.01, *** p<0.001, n.s., not significant.

(A) Bone marrow MKs (blue) and sinusoids (green) in control mice and mice 3 days after L. monocytogenes infection. CD41, blue; EMCN, green. Yellow arrows indicate MKs egressed into sinusoids. ‘S’ indicates sinusoid and dashed lines demarcate the border of sinusoids. (B) HSC and progenitor cell number in the bone marrow of control mice and mice 3 days after L. monocytogenes infection. LT, long term; ST, short term; MPP, multipotential progenitor. (C–D) MK numbers (C) and CXCR4low MK frequency (D) in the bone marrow of control mice and mice 3 days after L. monocytogenes infection (n=8 mice). (E–F) Frequency of BrdU+ (E) and Annexin V+ (F) cells in CXCR4high MKs from bone marrow of control mice or mice 3 days after L. monocytogenes infection (n=5 mice). Scale bars, 20µm. Data represent mean ± s.e.m. Two-tailed Student’s t-test was performed to assess statistical significance. * p<0.05, n.s., not significant.

(A) Schematic depicting the strategy of scRNA-seq using bone marrow MKs from mice 3 days after L. monocytogenes infection. (B) Violin plots showing the number of unique genes (gene number) before and after removing doublets, number of total unique molecular identifiers (UMI counts) and percentage of mitochondrial transcripts in single cells after removing doublets. Scatter plot showing the correlation between UMI counts and gene numbers. corr indicates Pearson correlation coefficient. (C) UMAP of the combined 5368 cells from control bone marrow and 4276 cells from L. monocytogenes infection bone marrow, colored by cell types. Neu, neutrophil; MP, myeloid progenitor; Mon, monocytes; B, B cells; T, T cells. (D) UMAP of the combined 1712 control MKs and 1560 infection MKs in the bone marrow, colored by clusters. (E) Fraction of each MK subpopulation from control MKs or L. monocytogenes infection MKs. (F) Violin plot showing Cxcr4 expression in each MK subpopulation of control and infection MKs.

(A–H) Relative expression of Cxcl12 (A–D) and frequency of CXCL12DsRed cells (E–H) in the bone marrow (A), lung (B), liver (C), and spleen (D) from control mice and mice 3 days after L. monocytogenes infection by RT-qPCR (A–D) and flow cytometry (E–H), respectively. Two-tailed Student’s t-test was performed to assess statistical significance. * p<0.05, ** p<0.01, *** p<0.001.

(A–B) Fraction of BrdU+ and Annexin V+ MKs in CXCR4high MKs in the liver (A) and spleen (B) from control mice or mice 3 days after L. monocytogenes infection. (C) Quantification of CXCR4low MKs in peripheral blood, liver, spleen, and lung of control mice and mice at 3 days after L. monocytogenes infection. (D) Quantification of CXCR4 levels on CXCR4high MKs and CXCR4low MKs treated with LPS or IFN-γ for 18 or 24 hr. (E–F) Representative images and quantification of mGFP+ MKs (green) in the liver (E) and spleen (F) from control mice or mice 3 days after L. monocytogenes infection (n=4 and 7 mice, respectively). (G–H) Gating strategies for CXCR4high MKs in the liver (G) and spleen (H). (I) Quantification of tdTomato+CXCR4low MKs in the liver and spleen from control mice or mice 3 days after L. monocytogenes infection by flow cytometry. Scale bars, 20μm. Data represent mean ± s.e.m. Two-tailed Student’s t-test was performed to assess statistical significance except (D). * p<0.05, ** p<0.01, *** p<0.001, n.s., not significant. Repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons in (D), ǂǂ p<0.01, ǂǂǂ p<0.01.

(A) Gene set variation analysis (GSVA) of each MK subpopulation under control or L. monocytogenes infection, colored by row-scaled GSVA enrichment scores. (B) Antigen processing and presentation score of control MK5, infection MK5, and bone marrow and lung MKs from Yeung et al., 2020. (C) Heatmap showing the row-scaled antigen processing and presentation gene expression of control and infection MK5 cells, comparing with bone marrow MK and lung MK from Yeung et al., 2020. Data represent mean ±first and third quartiles in (B). Two-sample KS test was performed to assess statistical significance in (B).

(A) Schema for transfer experiments using tdTomato+ MKs from Pf4+; Rosa26- mice into recipients 1 day following L. monocytogenes infection. (B) Transfused MKs (red) and sinusoids (green) in the lung of mice after L. monocytogenes infection as indicated in Figure 4M. DAPI, blue; EMCN, green; tdTomato, red. (C) Quantification of transfused intravascular and extravascular MKs in the lung. Scale bar, 20μm. Data represent mean ± s.e.m. Two-tailed Student’s t-test was performed to assess statistical significance. *** p<0.001.

Figure 4—figure supplement 1.

The effects of association of MKs and blood vessels, HSC activation, and MK numbers in bone marrow upon bacterial infection.

Bacterial infection stimulates the migration of CXCR4high MKs.

The effects of association of MKs and blood vessels, HSC activation, and MK numbers in bone marrow upon bacterial infection.

scRNA-seq of MKs from mice upon bacterial infection.

CXCL12 expression upon bacterial infection.

Cell cycle and apoptosis of CXCR4high MKs, and CXCR4low MK numbers in different organs upon bacterial infection.

Comparison of immune gene expression in MK5 and lung MKs from control mice or mice upon bacterial infection.

Intravascular and extravascular tdTomato+ MKs in the lung after MK perfusion.

MK migration traced by ex vivo live imaging.

MK (red, arrowhead) migrating into the bone marrow sinusoids (green, labeled by Dextran-FITC) by live imaging in the bone marrow of Pf4+; Rosa26-mice 24 hr after L. monocytogenes infection. Scale bar, 10μm. To further explore the dynamic migration of MKs upon pathogen infection, we adapted an ex vivo real-time imaging method to trace MK migration in the bone marrow (Xie et al., 2009). Using Pf4 mice and ex vivo live imaging approach, we observed that small tdTomato+ MKs rapidly migrated into sinusoids without rupture or platelet release upon infection (Figure 4F, Figure 4—video 1). In contrast, MKs with large sizes showed much slower migration (Figure 4—video 1). Additionally, CXCR4high MKs were decreased in the bone marrow 3 days after L. monocytogenes infection but with a similar proliferation and apoptosis rate compared to CXCR4low MKs (Figure 4G; Figure 4—figure supplement 1C-F), indicating CXCR4high MKs might migrate out of bone marrow. Consistent with this, the frequency of MK5, which enriched CXCR4high MKs, decreased in bone marrow after L. monocytogenes infection in our single-cell atlas (Figure 4—figure supplement 2). Furthermore, we found that L. monocytogenes infection decreased the expression of CXCL12, the ligand of CXCR4 (Sugiyama et al., 2006), in bone marrow but increased CXCL12 expression in the lung, liver, and spleen (Figure 4—figure supplement 3), suggesting that the distinguished CXCL12 levels between tissues might drive the migration of CXCR4high MKs between tissues. In line with this, CXCR4high MKs were increased in the peripheral blood and organs, including the liver, spleen, and lung 3 days after L. monocytogenes infection without an alternation of cell cycle and apoptosis, whereas CXCR4low MKs did not differ except for a slight increase in the liver (Figure 4G; Figure 4—figure supplement 4A-C). Moreover, inflammatory stresses, such as IFNγ and Lipopolysaccharides (LPS), or L. monocytogenes treatment did not increase CXCR4 expression in CXCR4low MKs (Figure 4H; Figure 4—figure supplement 4D).

Figure 4—video 1.

MK migration traced by ex vivo live imaging.

Figure 4—figure supplement 2.

scRNA-seq of MKs from mice upon bacterial infection.

Figure 4—figure supplement 3.

CXCL12 expression upon bacterial infection.

Figure 4—figure supplement 4.

Cell cycle and apoptosis of CXCR4high MKs, and CXCR4low MK numbers in different organs upon bacterial infection.

To further explore how MKs migrate between organs during bacterial infection in vivo, we employed Pf4, and Pf4 (Rosa26) mice in which Tomato or cell membrane-localized EGFP (mGFP) were exclusively expressed in MK lineage (Tiedt et al., 2007). mGFP expressing MKs or Tomato expressing CXCR4high MKs were increased in the liver and spleen 3 days after L. monocytogenes infection (Figure 4I; Figure 4—figure supplement 4E-H), whereas Tomato expressing CXCR4low MKs did not change (Figure 4—figure supplement 4I). To further confirm the tissue infiltration of MKs upon infection, we intravenously injected membrane-localized tdTomato (mTomato) expressing bone marrow cells from Rosa26R mice into control recipients or recipients infected with L. monocytogenes 1 day before mTomato+ cell perfusion (Figure 4J). We found that 2 days after mTomato+ cell perfusion, engrafted mTomato+ CXCR4high MKs more efficiently infiltrated into the liver (92.1%) and spleen (92.5%); by contrast, most mTomato+ CXCR4low MKs (66.7%) migrated to the bone marrow (Figure 4K). As the lung is an important site for platelet generation (Lefrançais et al., 2017), we aligned our MK sc-RNAseq data with lung MKs (Pariser et al., 2021; Yeung et al., 2020), and found that MK5, MK4, and MK3 showed similar gene profiles with lung MKs (Figure 4L). Moreover, MK5 enriched more inflammatory pathway genes, antigen processing, and presentation pathway after L. monocytogenes infection, which enabled MK5 to achieve a more similar transcriptional profile as the lung MKs than normal MK5 (Figure 4—figure supplement 5). Interestingly, we found that engrafted Tomato+ MKs (from Pf4 mice) more efficiently infiltrated the lungs in the infected recipients as extravascular MKs than in the control recipients (Figure 4M–O and Figure 4—figure supplement 6).

Figure 4—figure supplement 5.

Comparison of immune gene expression in MK5 and lung MKs from control mice or mice upon bacterial infection.

Figure 4—figure supplement 6.

Intravascular and extravascular tdTomato+ MKs in the lung after MK perfusion.

Acute inflammation-induced emergency megakaryopoiesis generates CXCR4high MKs upon infection

Infection-induced emergency megakaryopoiesis compensates the platelet consumption (Verschoor et al., 2011). Consistently, we observed that MKs were ruptured in the bone marrow 3 days after L. monocytogenes infection to recover the reduced platelets post-L. monocytogenes infection (Couldwell and Machlus, 2019; Nishimura et al., 2015; Figure 5A–C). However, CXCR4high MKs were increased at 18 hr after L. monocytogenes infection and substantially declined at 72 hr in bone marrow, whereas CXCR4low MKs remained unchanged upon infection (Figure 5D). As MK-committed HSCs drive infection-induced emergency megakaryopoiesis (Haas et al., 2015), we asked whether emergency megakaryopoiesis also generates CXCR4high MKs to participate in the host-defense response. To this aim, we employed Scl mice (Göthert et al., 2005) to monitor the HSPC derived emergency megakaryopoiesis upon bacterial infection. Eighteen hours after tamoxifen recombining Tomato in HSPCs and L. monocytogenes infection (Figure 5E), we observed that Tomato+ HSPCs derived Tomato+ CXCR4high MKs rapidly increased in the bone marrow, similar to the platelet-generating MKs (tdTomato+ CXCR4low MKs) (Figure 5F), without a noticeable rise of hematopoietic progenitors (Figure 5G). Overall, our observations indicated that CXCR4high MKs might be generated by emergency megakaryopoiesis to stimulate pathogen defense.

Figure 5.

Acute inflammation induces emergency megakaryopoiesis of CXCR4high MKs.

(A–B) Representative images (A) and statistical analysis (B) of CD41 (green) and DAPI (blue) in bone marrow from control mice or mice 3 days after L. monocytogenes infection. Arrows indicate ruptured MKs, yellow boxes indicate the locations of the magnified images. (C) Platelets in peripheral blood in control mice or mice after L. monocytogenes infection on indicated days. (D) The dynamics percentage of CXCR4high MKs (left) or CXCR4low MKs (right) in the bone marrow of L. monocytogenes-challenged mice within 72 hr of infection. (E) Schema for HSC lineage tracing upon L. monocytogenes infection using Scl+; Rosa26+/- mice. (F–G) Cell numbers of tdTomato+ CXCR4low MKs and tdTomato+ CXCR4high MKs (F), and tdTomato+ progenitors (G) in the bone marrow of control and L. monocytogenes infected Scl+; Rosa26+/- recipients 18 hr after L. monocytogenes infection and tamoxifen administration. CMP, common myeloid progenitor; GMP, granulocyte-monocyte progenitor; MEP, megakaryocyte-erythroid progenitor. Scale bars, 20 μm. Data represent mean ± s.e.m. Two-tailed Student’s t-test was performed to assess statistical significance, * p<0.05, ** p<0.01, n.s., not significant.

Acute inflammation induces emergency megakaryopoiesis of CXCR4high MKs.

Discussion

MKs participate in megakaryocyte maturation, platelet activation, and potentially influence neutrophils and the adaptive immune cells (Cunin and Nigrovic, 2019). Accordingly, MKs prevent the spread of dengue virus infection by enhancing the type 1 interferons pathway in murine and clinical biospecimens (Campbell et al., 2019) and contribute to cytokine storms in severe COVID-19 patients (Bernardes et al., 2020; Ren et al., 2021; Stephenson et al., 2021). MKs were reported to express multiple inflammation receptors, such as Fcγ receptors (Markovic et al., 1995), Toll-like receptors (Beaulieu et al., 2011; Ward et al., 2005), interleukin receptors (Navarro et al., 1991; Yang et al., 2000), and IFN receptors (Negrotto et al., 2011), which might enable MKs to receive inflammation signals and express cytokines. Recent scRNA-seq studies suggested the existence of MK subpopulations for inflammation responses (Liu et al., 2021; Pariser et al., 2021; Sun et al., 2021; Wang et al., 2021a). Here, we identified that MK5 has both MK and immune cell characteristics for platelet generation and immune responses. More importantly, we demonstrated that CXCR4high MKs recruited and stimulated innate myeloid cells by producing TNFα and IL-6, for bacterial phagocytosis. Furthermore, CXCR4high MKs had the ability for antigen processing and antigen presentation capacity, which suggested that CXCR4high MKs might contribute to the regulation of adaptive immune function. This is consistent with a previous observation that lung MKs are able to process and present antigens (Pariser et al., 2021). Our data suggested that CXCR4high MKs might contribute to the regulation of adaptive immune function. However, as the distinction between CXCR4high MKs and CXCR4low MKs is not entirely objective, additional markers are warranted to further enrich CXCR4high MKs. We observed that MK ablation increased HSPCS and myeloid granulocyte/macrophage progenitor (GMP) in bone marrow under bacterial infection, which is consistent with 5-FU stress (Hérault et al., 2017). However, increased GMP only increased myeloid cells in the bone marrow but not in other organs, which further supported the role of CXCR4high MKs in promoting the migration of myeloid cells. Normal HSC to MK development takes 11–12 days in humans and 4 days in mice; However, emergency megakaryopoiesis takes less than a day to generate MKs upon inflammation stress (Couldwell and Machlus, 2019; Liu et al., 2021; Sun et al., 2021; Figure 5D). Previously, researchers believed that emergency megakaryopoiesis mainly contributes to the replenishment of damaged platelets upon acute inflammation (Haas et al., 2015). We found that inflammation signals could not upregulate CXCR4 in CXCR4low MKs in vitro, although we cannot entirely exclude the plasticity of MKs in vivo. Our data showed that CXCR4high MKs might be generated from the emergency megakaryopoiesis, instead of CXCR4low MKs, to facilitate host-defense responses against bacterial infection. A recent report showed that the lung is a reservoir of MKs for platelet production (Lefrançais et al., 2017). Other works also indicate that lung MKs share a similar transcriptional profile with lung DCs and participate in pathogen infection (Boilard and Machlus, 2021; Pariser et al., 2021). However, the correspondence between MKs in the lung and bone marrow remains unexplored. Neonatal lung MKs lack the immune molecules in adult lung MKs (Pariser et al., 2021), which indicates that lung MKs might have distinct developmental origins. Similarly, MKs are observed to egress and migrate to the pulmonary capillary under stresses (Davis et al., 1997). Our works suggested lung MKs might migrate from bone marrow upon infection challenges, although more detailed investigations are warranted in future studies.

Materials and methods

Mice

C57BL/6-Tg(Pf4-cre)Q3Rsko/J (Pf4), C57BL/6-Gt(ROSA) 26Sortm1(HBEGF) Awai/J (Rosa26), C57BL/6-Gt(ROSA)26Sortm4(ACTB-tdTomato,-EGFP)Luo/J (Rosa26), Gt(ROSA)26Sortm9(CAG-tdTomato)Hze (Rosa26), CXCL12tm2.1Sjm/J (Cxcl12) and C57BL/6-Tg(TcraTcrb)1,100Mjb/J (OT-I) mice were obtained from the Jackson Laboratory. Scl mice were provided by J. R. Göthert. All mice were maintained in the C57BL/6 background. Animals were blindly included in the experiments according to genotyping results as a mix of male and female. All animal experiments were performed according to protocols approved by the Sun Yat-sen University animal care and use committee (approval No. SYSU-IACUC-2021-B0617).

Cell line

B3Z hybridoma T cells were kindly gifted by Dr. Nilabh Shastri (Johns Hopkins University). This cell line was verified to be mycoplasma free by EZdetect PCR Kit for Mycoplasma Detection (HiMedia).

Bacteria and infections

Listeria (L.) monocytogenes infection was performed as described with minor modifications (Edelson and Unanue, 2000; Verschoor et al., 2011). In brief, wild-type L. monocytogenes strain 10,403 S grown to exponential phase at 37 °C in TSB media was injected intravenously at a dose of 2500 colony-forming units (CFUs) to determine spleen and liver bacterial burdens 3 days after infection. Recombinant L. monocytogenes expressing the chicken ovalbumin peptide (OVA257-264) (L.m. – OVA257-264) was injected intravenously at a dose of 2500 CFUs to determine activated spleen T cells 7 days after infection. Escherichia (E.) coli wild-type strain 85,344 expressing GFP was constructed as previously described (Feng et al., 2020). GFP-labeled E. coli was grown to exponential phase at 37 °C in LB media and washed with PBS before being suspended for phagocytosis assays.

Antibodies for flow cytometry analysis and cell sorting

For cell sorting and analysis, monoclonal antibodies to CD41 (MWReg30, eBioscience), CXCR4 (2B11, eBioscience), CD11b (M1/70, eBioscience), F4/80 (BM8, eBioscience), Gr-1 (RB6-8C5, Biolegend), Ly6C (HK1.4, Biolegend), CD11c (N418, eBioscience), CD45.1 (A20, eBioscience), CD45.2 (104, Biolegend), CD4 (GK1.5, eBioscience), CD8 (53–6.7, Biolegend), INF-γ (XMG1.2, Biolegend), IL4 (11B11, Biolegend), CD34 (RAM34, eBioscience), Sca-1 (D7, Biolegend), c-kit (2B8, Biolegend), CD135 (A2F10, Biolegend), CD3ε (145–2 C11, Biolegend), CD45R (RA3-6B2, Biolegend), TER-119 (Ter-119, Biolegend), IgM (II/41, eBioscience), FγRII (93, Biolegend), IL-7R (A7R34, Biolegend), TNFα (MP6-XT22, Invitrogen), IL-6 (MP5-20F3, Biolegend), OVA257-264 (SIINFEKL) peptide bound to H-2Kb (eBio25-D1.16 (25-D1.16), Invitrogen) and IL-2 (JES6-5H4, eBioscience) were used where indicated.

Flow cytometry and cell sorting

Bone marrow cells were isolated from mouse femora and tibiae as previously reported (Jiang et al., 2018). Splenocytes were mechanically dissociated in PBS with 2% FBS. Peripheral blood was collected from the retro‐orbital sinus and anticoagulated by K2-EDTA. Those three kinds of cells then underwent red blood cell lysis for 5 min using 0.16 M ammonium chloride solution. Liver cells were mechanically dissociated and lysed using 0.16 M ammonium chloride solution, followed by gradient sedimentation using a density reagent (LTS1077, TBD Science) following the manufacturer’s instruction. Cell sorting was performed using a cell sorter (MoFlo Astrios, Beckman Coulter) with a 100 μm nozzle at a speed of around 5000 cells s–1. For intracellular cytokine staining, cells were pretreated with Brefeldin-A (BFA, 10 μg ml–1) for 4 hr at 37℃ before staining. For MK antigen presentation detection, MKs were co-culture with 100 μg ml–1 soluble full-length OVA for 24 hr before staining. For IFNγ, LPS and L. monocytogenes treatment, cells were co-culture with 10 ng ml–1 IFN-γ or 30 μg ml–1 LPS for 4, 18, or 24 hr, or 106 L. monocytogenes for 4 hr in a 37℃ incubator before staining. Cell analysis was performed on either one of the flow cytometers (Attune NxT, Thermo Fisher; Cytek AURORA, Aurora).

Single-cell library construction and sequencing

Sorted CD41+ FSChigh single cells from four mice of a control MK group and an MK group from mice 3 days upon L. monocytogenes infection each were processed through the Chromium Single Cell Platform using the Chromium Single Cell 3’ Library and Gel Bead Kit v3 (PN-1000075, 10 x Genomics) and the Chromium Single Cell B Chip Kit (PN-1000074, 10 x Genomics) as the manufacturer’s protocol. In brief, over 7000 cells were loaded onto the Chromium instrument to generate single-cell barcoded droplets. Cells were lysed and barcoded reverse transcription of RNA occurred. The library was prepared by following amplification, fragmentation, adaptor, and index attachment then sequenced on an Illumina NovaSeq platform.

scRNA-seq processing

The scRNA-seq reads were aligned to the mm10 reference genomes, and unique molecular identifier (UMI) counts were obtained by Cell Ranger 3.0.2. Normalization, dimensionality reduction, and clustering were performed with the Seurat 3.0 R package (Butler et al., 2018). For the control and Listeria (L.) monocytogenes infection group, we loaded one 10 x Genomics well each and detected 5663 and 5948 cells that passed the Cell Ranger pipeline, respectively. To ruled out low quality cells, cells with >12% mitochondrial content or <200 detected genes were excluded with Seurat function subset (percent.mt <12 & nFeature_RNA >200). We ruled out doublets with default parameters of DoubletDecon R package, and 54 control cells and 939 L. monocytogenes infected cells were excluded. Following the standard procedure in Seurat’s pipeline, we identified 3272 MKs from control mice (1712 MKs) and mice with L. monocytogenes infection (1560 MKs) (3897 and 3449 immune cells were discarded, respectively) in combination with MetaNeighbor method. Preprocessed dataset normalization was performed by dividing the UMI counts per gene by the total UMI counts in the corresponding cell and log-transforming before scaling and centering. SCT normalization was performed with the script: object <- SCTransform(object, vars.to.regress = “"percent.mt”", verbose = FALSE). Signature genes of each cluster were obtained using the Seurat function FindMarkers with Wilcox test with fold change >1.5 and p value <0.05 after clustering. Heatmaps, individual UMAP plots, and violin plots were generated by the Seurat functions in conjunction with ggplot2 and pheatmap R packages. Similarities and UMAP projection between our scRNA-seq data and published datasets GSE152574 (Yeung et al., 2020), GSE158358 (Pariser et al., 2021), GSE137540 (Xie et al., 2020), GSE128074 (Hamey et al., 2021), or GSE132042 (Almanzar et al., 2020) were conducted by MetaNeighbor R package (Crow et al., 2018), iMAP.py and Symphony R package (Kang et al., 2021). iMAP integration was performed using the default parameters except n_top_genes = 2000, min_genes = 0, min_cells = 0, and n_epochs = 100 before doing dimensionality reduction using Uniform Manifold Approximation and Projection method (UMAP, n_neighbors = 30, n_pca = 30). Radar charts were generated with JavaScript written by Nadieh Bremer (https://www.visualcinnamon.com/). Euclidean distances denote the distances between the centroid of each cluster. Correlations were calculated based on normalized RNA values, with the function cor and the parameter ‘method = “spearman”’. Multiple testing correction using the function cor.test with the parameter “method = “spearman” and it was applied for Cxcr4 expression correlations. We calculated the similarities between MK1 to 5 with the published MK, immune cell, and myeloid progenitor datasets (Almanzar et al., 2020; Hamey et al., 2021; Pariser et al., 2021; Xie et al., 2020; Yeung et al., 2020) using scmap R package (Kiselev et al., 2018). Default parameters and 1000 features were used and threshold >0 was set. Cell-type matches are selected based on the highest value of similarities and the second-highest value which is not 0.01 less than the highest value across all reference cell types. Cytokine, inflammatory, chemokine, and antigen processing and presentation scores were evaluated with the AddModuleScore function of Seurat using genes from KEGG pathway ko04060, cytokine-cytokine receptor interaction; GO:0006954, inflammatory response; chemokine ligands from CellPhoneDB.mouse (Jin et al., 2021) and GO:0019882, antigen processing and presentation. Interaction analysis of MKs and immune cells were conducted by CellPhoneDB (Efremova et al., 2020) (transformed to human orthologous genes Davidson et al., 2020) and CellChat R package (Jin et al., 2021). Only interactions involving cytokines were shown. Gene Ontology (GO) analysis was performed using clusterProfiler R package (Yu et al., 2012) and visualized using enrichplot R package (Yu, 2019). Gene set enrichment analysis (GSEA) was performed using gsea R package (Subramanian et al., 2005) and visualized using enrichplot R package. Gene lists were pre-ranked by the fold change values of the differential expression analysis using Seurat function FindMarkers. Gene sets for GSEA were obtained from GO database (GO:0002367, cytokine production involved in immune response; GO:0006954, inflammatory response; GO:0008009, chemokine activity; GO:0022409, positive regulation of cell-cell adhesion; GO:0002275, myeloid cell activation involved in immune response). Gene set variation analysis (GSVA) was performed using GSVA R package (Hänzelmann et al., 2013). GSVA was performed to calculate GSVA score of indicated pathway genes in single cell datasets with the whole protein encoding genes after log normalization of expression values. Gene sets for GSVA were obtained from GO database (GO:0022409, positive regulation of cell-cell adhesion; GO:0002275, myeloid cell activation involved in immune response; GO:0002367, cytokine production involved in immune response; GO:0007596, blood coagulation; GO:0019882, antigen processing and presentation; GO:0034340: response to type I interferon; GO:0034341: response to interferon-gamma; GO:0045088, regulation of innate immune response; GO:0042742, defense response to bacterium; GO:0002819, regulation of adaptive immune response; GO:1903708, positive regulation of hemopoiesis).

Lung cells preparation for flow cytometry

Lungs were removed and digested as described with minor modifications (Lefrançais et al., 2017). In brief, removed lungs were placed in 1.5 ml tubes, minced with scissors, and digested with 1 ml digestion buffer (HBSS with 1 mg ml–1 collagenase D, 0.1 mg ml–1 DNase I, 25 mM HEPES, 2 mM L-glutamine, and 2% FBS) for 30 min at 37℃ before filtration through a 100 μm cell strainer and red blood cell lysis for 5 min. Samples were then filtered through 70 μm strainers and resuspended for subsequent surface marker staining for flow cytometry.

Megakaryocyte ablation induction

Pf4 mice were mated with the Rosa26 line to generate Pf4 mice. Diphtheria toxin (DT, Sigma-Aldrich) was injected intraperitoneally every day at a dose of 40 ng g–1 bodyweight into Pf4+; Rosa26+/– mice and their cre negative counterparts to induce megakaryocyte ablation as indicated.

Cre-ER recombinase induction

Scl mice were mated with the Rosa26 line to generate Scl mice. For induction of cre-ER recombinase, Scl, Rosa26+/– mice were injected with tamoxifen intraperitoneally once (2 mg in 0.1 ml corn oil; Sigma-Aldrich).

BrdU incorporation assay

5-Bromo-2-deoxyuridine (BrdU) was administered at a single dose of 125 mg kg–1 body mass by intraperitoneal injection. Whole bone marrow cells were collected 12 hr later and incubated with anti-CD41 and anti-CXCR4 for 1 hr. Cells were washed and then fixed with 4% PFA at 4 °C overnight. Cells were then permeabilized with 0.5% TritonX-100 for 15 min at room temperature and incubated with 1 mg ml–1 DNase I (Roche) for 1 hr at 37 °C followed by incubating with anti-BrdU (BU20A, eBioscience) for 1 hr at room temperature before being analyzed.

Annexin V binding assay

For Annexin V binding assay, bone marrow cells were incubated with cell surface markers for 1 hr at 4 °C and then washed with PBS before being resuspended with Annexin V binding buffer (Biolegend). Cells were then incubated with FITC Annexin V (Biolegend) for 15 min at room temperature in dark, and then 300 μl Annexin V binding buffer was added to each tube. Cells were analyzed by a flow cytometer.

Immunostaining

Immunostaining of frozen sections was performed as described (Jiang et al., 2018). For bone sections, mice were perfused with PBS and 4% paraformaldehyde (PFA). Then the bones were fixed with 4% PFA for 24 hr, decalcified with 0.5 M EDTA for 2 days, and gradient dehydrated by 15% and 30% sucrose for another 2 days. The thick of sections was 30 μm. We used CD41 (MWReg30; eBioscience; 1:200), Endomucin (R&D; 1:100), CD150 (TC15-12F12.2; Biolegend; 1:100), CD48 (HM48-1; Biolegend; 1:100), CXCR4 (2B11, eBioscience; 1:100) antibodies, and lineage panel (Biolegend; cat #133307; 1:50). Secondary staining was done with donkey anti–goat AlexaFluor 488 (Invitrogen; 1:1000). For the liver and spleen from Pf4+/– mice, and lung from Pf4-cre+/– mice, we used DAPI (Thermo Fisher; 0.5 μg ml–1) to stain the frozen sections. For phagocytosis analysis, F4/80 (BM8, eBioscience; 1:100), CD11b (M1/70; Invitrogen; 1:100), CD41 (MWReg30; Thermo Fisher; 1:200) and DAPI was used. For sorted MKs, we used CXCR4 (2B11, eBioscience; 1:100), TNFα (R023, Sino Biological; 1:100) and IL-6 (MP5-20F3, Biolegend; 1:100) antibody. Secondary staining was performed with donkey anti-rabbit AlexaFluor 488 (Invitrogen; 1:1000). Confocal images were obtained using a spinning-disk confocal microscope (Dragonfly, Andor) and analyzed using Imaris 9.0 software (Oxford Instruments). Three-Dimension plots were generated using Matplotlib (Hunter, 2007).

Quantitative real-time (qRT-) PCR

For RT-qPCR, MKs were dissociated in Trizol (Magen), and RNA was extracted following the manufacture’s instruction. RNA was reverse transcribed into cDNA using the TransCript All-in-One First-Strand cDNA Synthesis kit (Transgene). Quantitative PCR was performed using a Bio-Rad CFX 96 touch. The primers for Pf4 were 5’-GGGATCCATCTTAAGCACATCAC-3’ (forward) and 5’-CCATTCTTCAGGGTGGCTATG-3’ (reverse). The primers for Vwf were 5’-CTTCTGTACGCCTCAGCTATG-3’ (forward) and 5’-GCCGTTGTAATTCCCACACAAG-3’ (reverse). The primers for Mpl were 5’-AACCCGGTATGTGTGCCAG-3’ (forward) and 5’-AGTTCATGCCTCAGGAAGTCA-3’ (reverse). The primers for Cxcl12 were 5’-AGGTTCTTATTTCACGGCTTGT-3’ (forward) and 5’-TGGGTGCTGAGACCTTTGAT-3’ (reverse). The primers for Gapdh were 5’-AGGTCGGTGTGAACGGATTTG-3’ (forward) and 5’-GGGGTCGTTGATGGCAACA-3’ (reverse). Gapdh was used as the reference gene for qRT-PCR analysis.

Transwell migration

Transmigration assays were performed on a transwell with a pore size of 5 μm (Biofil). CXCR4low MKs or CXCR4high MKs from bone marrow were sorted (5000 cells per well) from control mice and added to the lower chamber with 600 μl IMDM (Thermo Fisher) plus 10% FBS (Gibco). Peripheral blood cells were collected as described in the ‘Flow cytometry and cell sorting’ section. 6×105 peripheral blood cells were resuspended in 100 μl RPMI 1640 (Gibco) plus 10% FBS and added to the upper insert to continue for 2-hr incubation at 37 °C, 5% CO2. Cells in the lower chamber were harvested, washed with PBS once, and resuspended with 100 μl PBS for staining and FACS counting.

Phagocytosis

Bone-marrow-derived macrophages (BMDM) from C57BL/6 mice at 6–8 weeks of age were differentiated from bone marrow precursors with minor modifications (Minutti et al., 2019). In brief, bone marrow cells were isolated and propagated for 7 days in DMEM without sodium pyruvate or HEPES (Gibco), containing 20% FBS (Gibco), 30% supernatants of L929 conditioned media, and 1% Pen/Strep (Hyclone) at 37 °C. Macrophage phagocytosis assays were performed on a transwell plate with a pore size of 3 μm (Biofil) as described with modifications (Sharif et al., 2014). Briefly, attached cells were replated into 24-well plates, 5×104 cells per well, on glass coverslips for 24 hr culture. Then 5000 sorted CXCR4low MKs or CXCR4high MKs were added in the upper inserts and placed onto macrophages chambers for additional 16 hr incubation without or with 2 μg ml–1 TNFα neutralizing antibody (R023, Sino Biological; 1:100) or 2 μg ml–1 IL-6 neutralizing antibody (MP5-20F3, Biolegend) at 37 °C, 5% CO2. The upper inserts were discarded and macrophages were washed with PBS without antibiotics and incubated with 105 GFP-labeled E. coli for 2 hr at 37 °C, 5% CO2. Cells were washed three times with PBS and incubated with DMEM without sodium pyruvate or HEPES (Gibco) with gentamycin (50 μg ml–1) for 30 min at 37 °C, 5% CO2 to remove adherent bacteria. Cells were then detected by flow cytometry or fixed by cold methanol for 15 min and blocked with 10% BSA overnight, followed by incubation with F4/80 (BM8, eBioscience; 1:100) for 2 hr at room temperature before being quantified using a spinning disk confocal microscope (Dragonfly, Andor). For neutrophil phagocytosis, CD11b+ Gr1+ Ly6c– neutrophils were sorted from the spleen and propagated in RPMI 1640 (Gibco) containing 10% FBS. Neutrophil phagocytosis was performed as described in macrophage phagocytosis assay, except cells were sedimented for 30 min and fixed on glass coverslips after incubated with GFP-E. coli and gentamycin. The capacity of phagocytosis was evaluated by flow cytometry or by fluorescence intensity of GFP-E. coli. using the confocal microscope within macrophages and neutrophils. For megakaryocyte phagocytosis, CXCR4low and CXCR4high MKs were sorted from the bone marrow and propagated in RPMI 1640 (Gibco) containing 10% FBS without antibiotics and incubated with 105 GFP-labeled E. coli for 2 hr at 37 °C, 5% CO2. Cells were washed three times with PBS and incubated with DMEM without sodium pyruvate or HEPES (Gibco) with gentamycin (50 μg ml–1) for 30 min at 37 °C, 5% CO2 to remove adherent bacteria. Cells were then detected by flow cytometry.

Bone marrow ex vivo live imaging

Pf4+; Rosa26+/– mice were infected with L. monocytogenes for 24 hr. FITC-Dextran (average mol wt 2000000, Sigma-Aldrich) was injected intravenously at a dose of 1.25 mg per mouse before being sacrificed. The ends of the femur below the end of the marrow cavity were cut. The bone marrow plug was gently flushed out of the end of the bone with a 21-gauge blunt needle not to break up the marrow plug. Bone marrow was flushed integrally and fixed onto a glass slide in a chamber, rinsed with RPMI 1640 (Gibco), and covered slightly with a coverslip. The integrity of the vascular structure in the bone marrow was observed and warranted through FITC-Dextran inflorescence before capturing images. Confocal images were obtained every minute on the spinning-disk confocal microscope (Dragonfly, Andor) and analyzed using Imaris 9.0 software (Oxford Instruments).

In vitro MK culture, MK size, and proplatelet formation measurement

MKs were sorted using a cell sorter (MoFlo Astrios, Beckman Coulter) and cultured in 24-well plates in SFEM (Stem Cell Technologies) plus 100 ng ml–1 mTPO (Novoprotein) and 1% Pen/Strep (Hyclone), and incubated at 37 °C, 5% CO2 for 4 days. Images were taken by a Nikon Ts2R microscope equipped with a Nikon DS-Ri2 camera. Cell size and proplatelet formation were measured on day 3 or day 5 post-cultured, respectively, using Nikon NIS-Elements BR.

Bone marrow transfer experiments

Pf4 mice were mated with the Rosa26 line to generate Pf4+; Rosa26+/– mice. tdTomato+ MKs were isolated from Pf4+; Rosa26+/– mice. Six- to 8-week-old recipient mice were pre-treated with PBS or 2500 CFUs of L. monocytogenes as previously described 1 day before cell perfusion. 1×105 tdTomato+ MKs were sorted and intravenously injected into control or L. monocytogenes infected mice. tdTomato+ MKs were detected in lungs with immunostaining 2 days after cell perfusion. mtdTomato+ bone marrow cells were isolated from Pf4–; Rosa26+/– mice. 1×106 mtdTomato+ bone marrow cells were intravenously injected into control or one-day-L. monocytogenes infected mice. mtdTomato+ MKs were detected in bone marrow, liver, and spleen 2 days after cell perfusion. For in vivo CXCR4high MK function assay in MK ablation mice, DT was intraperitoneally injected every day for 5 days. On the second and fourth days, 2×105 sorted wild-type CXCR4high MKs or CXCR4low MKs were intravenously injected into indicated groups. PBS or 2500 CFUs of L. monocytogenes as previously described were injected intravenously on the third day. Spleen and liver were harvested 3 days after infection to determine the bacterial burdens as described.

T cell reactivation in vitro

Splenocytes (1×106 cells well–1) from control or MK ablated mice after 7 days L.m.-OVA infection were re-stimulated for 4 hr in vitro with OVA peptide (10 μM) in the presence of Brefeldin-A (BFA, 10 μg ml–1). Activated T cells were then analyzed by a flow cytometer. For MK-induced T cell activation, 3×104 MK subpopulations for each sample were sorted and co-cultured with 100 μg ml–1 soluble full-length OVA for 24 hr, then co-cultured with 6×104 OT-I CD8+ T cells or B3Z T cells (Karttunen et al., 1992) for 48 hr at 37 °C in a 5% CO2 incubator as described (Zufferey et al., 2017). OT-I T cell activation was detected by measuring intracellular IL-2 levels. B3Z T cell activation was detected using β-galactosidase Assay Kit (RG0036, Beyotime). Bone marrow-derived dendritic cells (DCs) were adopted as positive controls for T cell activation assay. To obtain bone marrow-derived DCs, isolated bone marrow cells were cultured in RPMI 1640 with 10 ng ml–1 of GM-CSF and 10 ng ml–1 of IL-4 as described (Roney, 2019).

Computational modeling of random myeloid cell localization

We have performed randomized simulations as in previous reports (Bruns et al., 2014; Jiang et al., 2018) in Python. Images of a 400 μm × 400 μm bone marrow region with CXCR4high and CXCR4low MKs, in which background staining was removed, were used to generate MKs onto which 200 myeloid cells were randomly placed, consistent with an average density of 200 myeloid cells per field. Each simulated run placed 200 random myeloid cells (mean diameter 5 μm) was repeated 500 times. The shortest Euclidean distance was calculated for each myeloid cell to CXCR4high or CXCR4low MKs. Random and observed distance distributions were compared using the modified nonparametric two-dimensional (2D) KS test as described (Bruns et al., 2014; Jiang et al., 2018).

Statistical analyses

Data are presented as means ± s.e.m or presented medians, first and third quartiles. For phagocytosis assay and MK size measurement, data were analyzed by a one-dimensional KS test. Differences were considered statistically significant if p<0.05. For the comparison of three-dimensional distances, a two-dimensional KS test was used. The difference was considered statistically significant if p<0.05. For multiple comparisons analysis, data were analyzed by repeated-measures one-way analysis of variance (ANOVA) followed by Dunnett’s test. Differences were considered statistically significant if p<0.05. ǂ p<0.05, ǂǂ p<0.01, ǂǂǂ p<0.001, n.s., not significant. For pairs of measurements, data were analyzed by paired Student’s t-test. Differences were considered statistically significant if p<0.05. # p<0.05, ## p<0.01, ### p<0.001, n.s., not significant. For other experiments except for scRNA-seq analysis, data were analyzed by a two-tailed Student’s t-test. Differences were considered statistically significant if p<0.05. * p<0.05, ** p<0.01, *** p<0.001, n.s., not significant. The manuscript by Wang and colleagues studies the heterogeneity of megakaryocytes using single-cell RNA-seq and identifies a subpopulation of CXCR4-high megakaryocytes with immune modulatory roles. Functional studies in this paper show that this subpopulation of megakaryocytes promotes bacterial phagocytosis by macrophages and neutrophils. The compelling evidence for these findings in the manuscript and the fundamental significance of identifying mechanisms by which megakaryocyte subpopulations can impact host defense make this work of interest to researchers in the fields of immunology, hematopoiesis and megakaryocyte biology. Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work. Decision letter after peer review: Thank you for submitting your article "Megakaryocyte derived immune-stimulating cells regulate host-defense immunity against bacterial pathogens" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Jalees Rehman as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jos van der Meer as the Senior Editor. The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Essential revisions: 1. Please clarify whether these are Megakaryocyte-derived immune cells (MDICs) or simply a subpopulation of megakaryocytes (MK5). The presented data does not provide any evidence for the "derivation" process such as differentiation factors that convert the megakaryocyte subpopulation MK5 into a different "immune cell". Therefore, it is requested to change the MDIC term and instead use an expression that indicates that these cells are a subset of megakaryocytes. 2. Please perform studies to ascertain the phagocytosis potential of this megakaryocyte subset. Are they capable of direct bacterial phagocytosis in the absence of other immune cells or do they only enhance bacterial phagocytosis by other immune cells? 3. Does this CXCR4 high megakaryocyte subset overlap with other megakaryocyte subsets demonstrating immune cell functions that have been recently reported such as antigen processing or antigen presentation? Assessing their antigen processing or antigen presentation capacity would help define the roles of this MK subset in host defense. 4. Does the CXCR4 high megakaryocyte subset also generate platelets or is their platelet generation function diminished compared to other megakaryocyte subsets, thus indicating that they are more specialized for immune functions. 5. How is this CXCR4 subset generated and what is their fate after infusion? Does CXCR4 get upregulated during infection on non-CXCR4 megakaryocytes? This would suggest that they are not terminally programmed to be CXCR4 high but that other megakaryocyte subsets can become MK5/CXCR4 high and take on such host defense functions. 6. Conclusions and generalizations need to be adjusted based on the comments of the reviewers as provided below. One specific example is the need to moderate claims about the role of megakaryocytes in adaptive immunity as this was not addressed in depth. Other examples of conclusions that need to be moderated are also listed by the reviewers in their specific comments. 7. Some technical details need to be addressed such as clarifying flow cytometry gating strategies or describing the new imaging approach. Description of the imaging approach and objective quantification of imaging data is essential to increase the robustness of the conclusions. The goal is for readers to be able to understand and replicate the experiments. More protein level quantifications of cytokines using flow cytometry or ELISA are also required to justify several of the conclusions. Reviewer #1 (Recommendations for the authors): Specific suggestions to include in the manuscript would include: 1. Perform antigen processing assays with CXCR4 high and CXCR4 low MKs to then assess whether this is the subset that drives the adaptive immune modulatory function 2. Use CXCR4 low cells and expose them ex vivo to inflammatory or infectious stimuli to assess whether they can become CXCR4 high. This would assess the plasticity of the MK subsets. Reviewer #2 (Recommendations for the authors): Previous studies suggested that immune MKs are capable of phagocytosis themselves and not just supporting immune cells. Are MDIC cells capable of direct bacterial phagocytosis? In Figure 2, it's unclear if the myeloid cell-CXCR4high MK association is simply a result of the expansion of myeloid cells upon infection. Is this association observed in homeostasis? What happens to CXCR4 expression on MKs upon infection? It would be important to understand if CXCR4low MKs are "reprogrammed" into CXCR4high MKs upon infection. Other technical concerns: – Elisa or flow cytometry analyses would be more accurate to evaluate TNFα, IL-6, and CXCL12 levels. – The authors describe a new method of "bone marrow live imaging", however it's unclear how imaging of flushed bone marrow plugs ex vivo accurately reflects live imaging. – It's unclear the gating strategy used to quantify bone marrow, liver and spleen myeloid cell populations in Figure 3 as some of these populations rely on tissue specific markers. – In Figure 4 supplemental 3, it's unusual to present CXCL12 levels as mRNA expression from total cells in the tissue, and it's even more surprising that the authors can detect accurate levels in organs such as the bone marrow or spleen since only small subsets of cells express CXCL12 in the tissue. – Small concern, in Figure 4G the results are presented in different formats alternating between absolute number quantification or percentages. Reviewer #3 (Recommendations for the authors): There are a number of specific things the authors can do to improve this manuscript. 1. The term MDIC should be reconsidered – especially the meaning of the D – are they Mk derived, or a subset of Mks? Since seem to be a subset, the derived is not really appropriate. 2. The authors indicate that the MDIC enhance the phagocytosis capacity of other cells – but do they also phagocytose bacteria? Is this contact dependent or independent to better show is a secreted product or not. 3. The DT model ablates both Mks and platelets. Therefore many of the changes in bacteria and immune cells could be platelet driven, not Mk or MDIC driven. This must be reconciled or the conclusions drastically restated in Figure 3. 4. Where do the infused MDIC go? Marrow, spleen, lung or all of the above? Do they make platelets and if so better or worse than Mk1 or Mk2 populations? 5. Do any of the lung migrated Mk become extravascular lung Mks, as 2/3 of lung Mks are extravascular? Or do they stay intravascular platelet producing Mks? Essential Revisions (for the authors): 1. Please clarify whether these are Megakaryocyte-derived immune cells (MDICs) or simply a subpopulation of megakaryocytes (MK5). The presented data does not provide any evidence for the "derivation" process such as differentiation factors that convert the megakaryocyte subpopulation MK5 into a different "immune cell". Therefore, it is requested to change the MDIC term and instead use an expression that indicates that these cells are a subset of megakaryocytes. Thanks for the suggestion. We have changed the MDICs term to CXCR4high MKs. 2. Please perform studies to ascertain the phagocytosis potential of this megakaryocyte subset. Are they capable of direct bacterial phagocytosis in the absence of other immune cells or do they only enhance bacterial phagocytosis by other immune cells? Our revised data showed that CXCR4high MKs were capable of bacterial phagocytosis (Revised Figure 3F). 3. Does this CXCR4 high megakaryocyte subset overlap with other megakaryocyte subsets demonstrating immune cell functions that have been recently reported such as antigen processing or antigen presentation? Assessing their antigen processing or antigen presentation capacity would help define the roles of this MK subset in host defense. Our revised data showed that CXCR4high MKs were able to phagocytose bacteria (Revised Figure 3F), and process and present ovalbumin (OVA) antigens on their cell surface (Revised Figure 3G) to activate CD8+ OT-I T cells (Revised Figure 3H) and B3Z T cells (Revised Figure 3-S2), a T cell hybridoma which expresses TCR that specifically recognizes OVA. These revised data showed that CXCR4high MKs had antigen processing and antigen presentation capacity. 4. Does the CXCR4 high megakaryocyte subset also generate platelets or is their platelet generation function diminished compared to other megakaryocyte subsets, thus indicating that they are more specialized for immune functions. Our revised data showed that CXCR4high MKs also generated platelets but with lower efficiency compared to CXCR4low MKs (Revised Figure 1-S6D), suggesting CXCR4high MKs might be specialized for immune functions. 5. How is this CXCR4 subset generated and what is their fate after infusion? Does CXCR4 get upregulated during infection on non-CXCR4 megakaryocytes? This would suggest that they are not terminally programmed to be CXCR4 high but that other megakaryocyte subsets can become MK5/CXCR4 high and take on such host defense functions. Our revised data showed that inflammatory treatment, including interferon γ, LPS, and L. monocytogenes could not increase CXCR4 expression in CXCR4low MKs (Revised Figure 4H and Figure 4-S4D). This experiment suggested that CXCR4high MKs might not be reprogramed from CXCR4low MKs. Furthermore, our HSPC tracing experiment showed that CXCR4high MKs were generated from HSPCs as efficiently as CXCR4low MKs during the acute inflammation-induced emergency megakaryopoiesis (Revised Figure 5E-G). 6. Conclusions and generalizations need to be adjusted based on the comments of the reviewers as provided below. One specific example is the need to moderate claims about the role of megakaryocytes in adaptive immunity as this was not addressed in depth. Other examples of conclusions that need to be moderated are also listed by the reviewers in their specific comments. We have removed the conclusion about adaptive immunity as suggested. 7. Some technical details need to be addressed such as clarifying flow cytometry gating strategies or describing the new imaging approach. Description of the imaging approach and objective quantification of imaging data is essential to increase the robustness of the conclusions. The goal is for readers to be able to understand and replicate the experiments. More protein level quantifications of cytokines using flow cytometry or ELISA are also required to justify several of the conclusions. In this revision, we have added flow cytometry gating strategies (Revised Figure 3-S1C, E, F and Figure 4-S4G, H), and the description for the ex vivo live imaging approach in the method section (Line 7-18, Page 26). We have also performed flow cytometry assays to confirm the imaging assays for bacterial phagocytosis assays, quantification of MKs and cytokine expression (Revised Figure 2J, M, N, Figure 2-S2A, B, Figure 4I, O and Figure 4-S4I). Reviewer #1 (Recommendations for the authors): Specific suggestions to include in the manuscript would include: 1. Perform antigen processing assays with CXCR4 high and CXCR4 low MKs to then assess whether this is the subset that drives the adaptive immune modulatory function Thanks for the suggested experiment. Our revised data showed that CXCR4high MKs, but not CXCR4low MKs, were able to process and present ovalbumin (OVA) antigens on their cell surface (Revised Figure 3G) and activate T cells (Revised Figure 3H and Figure 3-S2). 2. Use CXCR4 low cells and expose them ex vivo to inflammatory or infectious stimuli to assess whether they can become CXCR4 high. This would assess the plasticity of the MK subsets. Our revised data showed that inflammatory treatment, including interferon γ, LPS, and L. monocytogenes could not increase CXCR4 expression in CXCR4low MKs (Revised Figure 4H and Figure 4-S4D). This experiment suggested that CXCR4high MKs might not be reprogramed from CXCR4low MKs, although we cannot entirely exclude the plasticity of MK subsets. We have included a brief discussion in the current version (Line 20-22, Page14). Reviewer #2 (Recommendations for the authors): Previous studies suggested that immune MKs are capable of phagocytosis themselves and not just supporting immune cells. Are MDIC cells capable of direct bacterial phagocytosis? Our revised data showed that MDICs (now referred to as CXCR4high MKs) were capable of phagocytosis for bacteria (Revised Figure 3F). In Figure 2, it's unclear if the myeloid cell-CXCR4high MK association is simply a result of the expansion of myeloid cells upon infection. Is this association observed in homeostasis? In this revision, we showed that CXCR4high MKs were significantly associated with myeloid cells upon infection compared with randomly distributed spots in the mathematical modeling (Revised Figure 2B-C). This may not be due to the expansion of myeloid cells, as the association between CXCR4low MKs and myeloid cells was not significant compared to randomly distributed spots (Revised Figure 2C). Furthermore, we did not observe the association between CXCR4high MKs and myeloid cells during homeostasis (Revised Figure 2-S1C, D). What happens to CXCR4 expression on MKs upon infection? It would be important to understand if CXCR4low MKs are "reprogrammed" into CXCR4high MKs upon infection. Thanks for the suggested experiment. Our revised data showed that inflammatory treatment, including Interferon γ, LPS, and L. monocytogenes could not increase CXCR4 expression in CXCR4low MKs (Revised Figure 4H and Figure 4-S4D). This experiment suggested that CXCR4high MKs might not be reprogramed from CXCR4low MKs, although we cannot entirely exclude the plasticity of MK subsets. We have included a brief discussion in the current version (Line 20-22, Page 14). Other technical concerns: – Elisa or flow cytometry analyses would be more accurate to evaluate TNFα, IL-6, and CXCL12 levels. Thanks for the suggested experiments. In this revision, we have further evaluated the expression of TNFα and IL-6 by flow cytometry, which consistently showed that CXCR4high MKs had higher expression levels of TNFα and IL-6 than CXCR4low MKs (Revised Figure 2J). We have also adapted Cxcl12 reporter mouse line to show that CXCL12 was upregulated in the liver and spleen but reduced in bone marrow upon infection by flow cytometry assays (Revised Figure 4-S3E-H). – The authors describe a new method of "bone marrow live imaging", however it's unclear how imaging of flushed bone marrow plugs ex vivo accurately reflects live imaging. Sorry for the inaccurate description. This an ex vivo imaging approach, which is developed before for the real-time imaging in the bone (Xie et al., Nature 2009). We have described this ex vivo imaging approach in the result and method section (Line 7-18, Page 26). – It's unclear the gating strategy used to quantify bone marrow, liver and spleen myeloid cell populations in Figure 3 as some of these populations rely on tissue specific markers. Thanks for pointing out this technical issue. To avoid inconsistency, we analyzed the CD11b+ Ly6c+ monocytes, CD11b+F4/80+Ly6c– macrophages, CD11c+CD11b+ dendritic cells, and CD11b+Ly6c–Gr-1+ neutrophils from bone marrow, liver, and spleen. The gating strategy has been used in previous studies (Krenkel et al., Gut 2020; Swirski et al., Science 2009). We have also included the representative gating strategies for myeloid cells (Revised Figure 3-S1C, E, F). – In Figure 4 supplemental 3, it's unusual to present CXCL12 levels as mRNA expression from total cells in the tissue, and it's even more surprising that the authors can detect accurate levels in organs such as the bone marrow or spleen since only small subsets of cells express CXCL12 in the tissue. We agree with this argument. In this revision, we employed a Cxcl12 reporter mouse line, and further confirmed that CXCL12 was upregulated in the liver and spleen but reduced in bone marrow upon infection (Revised Figure 4-S3E-H). – Small concern, in Figure 4G the results are presented in different formats alternating between absolute number quantification or percentages. Thanks for the suggestion. We presented these results in percentage in the current version (Revised Figure 4G). Reviewer #3 (Recommendations for the authors): There are a number of specific things the authors can do to improve this manuscript. 1. The term MDIC should be reconsidered – especially the meaning of the D – are they Mk derived, or a subset of Mks? Since seem to be a subset, the derived is not really appropriate. We agree with this argument. We have changed the MDIC term to CXCR4high MKs in the current version as suggested. 2. The authors indicate that the MDIC enhance the phagocytosis capacity of other cells – but do they also phagocytose bacteria? Is this contact dependent or independent to better show is a secreted product or not. Thanks for pointing out this issue. Our revised data showed that CXCR4high MKs were capable of bacterial phagocytosis, although the efficiency was relatively lower compared to neutrophils (Revised Figure 3F). Furthermore, CXCR4high MKs promoted the phagocytosis capacity of neutrophils and macrophages in the upper inserts of transwell plates (Revised Figure 2E-H and Figure 2-S2A, B), which indicated that CXCR4high MKs promote the phagocytosis capacity of other cells through a cell-contact independent manner. By using neutralizing antibodies, we also showed that CXCR4high MKs promote the phagocytosis of immune cells partially through secreting IL-6 and TNFα (Revised Figure 2K-N). 3. The DT model ablates both Mks and platelets. Therefore many of the changes in bacteria and immune cells could be platelet driven, not Mk or MDIC driven. This must be reconciled or the conclusions drastically restated in Figure 3. We agree with this argument. Our infusion rescue with CXCR4high MKs did not fully rescue the host-defense responses in MK ablated mice, which might be partially due to the reduced platelets, which are known for immune responses. We have discussed this possibility in the current version (Line 16-17, Page 9). 4. Where do the infused MDIC go? Marrow, spleen, lung or all of the above? Do they make platelets and if so better or worse than Mk1 or Mk2 populations? The infused MDICs (now referred to as CXCR4high MKs) preferably migrated to the liver and spleen; conversely, the infused CXCR4low MKs preferably migrated to the bone marrow (Revised Figure 4J-O). Furthermore, CXCR4high MKs produced fewer platelets than CXCR4low MKs, which enriched MK1 and MK2 populations (Revised Figure 1-S6D). 5. Do any of the lung migrated Mk become extravascular lung Mks, as 2/3 of lung Mks are extravascular? Or do they stay intravascular platelet producing Mks? Thanks for the suggested experiments. Our revised data showed that the lung migrated MKs were extravascular lung MKs (Revised Figure 4-S6). References Beaulieu, L. M., Lin, E., Morin, K. M., Tanriverdi, K., and Freedman, J. E. (2011). 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Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Antibody	Anti-CD41a (mouse monoclonal)	eBioscience	Cat#17-0411-82RRID:AB_1603237	FACS (1 μl per test)
Antibody	Anti-CXCR4 (mouse monoclonal)	eBioscience	Cat#53-9991-80 RRID:AB_953573	FACS (1 μl per test)
Antibody	Anti-CD11b (mouse monoclonal)	eBioscience	Cat#12-0112-82 RRID:AB_2734869	FACS (1 μl per test)
Antibody	Anti-F4/80 (mouse monoclonal)	eBioscience	Cat#17-4801-80 RRID:AB_2784647	FACS (1 μl per test)
Antibody	Anti-Gr-1 (mouse monoclonal)	Biolegend	Cat#108424 RRID:AB_2137485	FACS (1 μl per test)
Antibody	Anti-Ly-6C (mouse monoclonal)	Biolegend	Cat#128022 RRID:AB_10639728	FACS (1 μl per test)
Antibody	Anti-CD11c (mouse monoclonal)	eBioscience	Cat#12-0114-82 RRID:AB_465552	FACS (1 μl per test)
Antibody	Anti-CD45.1 (mouse monoclonal)	eBioscience	Cat#15-0453-82 RRID:AB_468759	FACS (1 μl per test)
Antibody	Anti-CD45.2 (mouse monoclonal)	Biolegend	Cat#109831 RRID:AB_10900256	FACS (1 μl per test)
Antibody	Anti-CD4 (mouse monoclonal)	eBioscience	Cat#12-0041-82 RRID:AB_465506	FACS (1 μl per test)
Antibody	Anti-CD8a (mouse monoclonal)	Biolegend	Cat#100707 RRID:AB_312746	FACS (1 μl per test)
Antibody	Anti-IFN-γ (mouse monoclonal)	Biolegend	Cat#505813 RRID:AB_493312	FACS (1 μl per test)
Antibody	Anti-IL-4 (mouse monoclonal)	Biolegend	Cat#504118 RRID:AB_10898116	FACS (1 μl per test)
Antibody	Anti-CD34 (mouse monoclonal)	eBioscience	Cat#11-0341-82 RRID:AB_465021	FACS (1 μl per test)
Antibody	Anti-Sca-1 (mouse monoclonal)	Biolegend	Cat#108114 RRID:AB_493596	FACS (1 μl per test)
Antibody	Anti-c-Kit (mouse monoclonal)	Biolegend	Cat#105812 RRID:AB_313221	FACS (1 μl per test)
Antibody	Anti-CD135 (mouse monoclonal)	Biolegend	Cat#135314 RRID:AB_2562339	FACS (1 μl per test)
Antibody	Anti-CD3ε (mouse monoclonal)	Biolegend	Cat#100310 RRID:AB_312675	FACS (1 μl per test)
Antibody	Anti-B220 (mouse monoclonal)	Biolegend	Cat#103210 RRID:AB_312995	FACS (1 μl per test)
Antibody	Anti-TER-119 (mouse monoclonal)	Biolegend	Cat#116210 RRID:AB_313711	FACS (1 μl per test)
Antibody	Anti-IgM (mouse monoclonal)	eBioscience	Cat#15-5790-82 RRID:AB_494222	FACS (1 μl per test)
Antibody	Anti-CD16/32 (mouse monoclonal)	Biolegend	Cat#101333 RRID:AB_2563692	FACS (1 μl per test)
Antibody	Anti-CD127 (mouse monoclonal)	Biolegend	Cat#135021 RRID:AB_1937274	FACS (1 μl per test)
Antibody	Anti-TNFα (mouse monoclonal)	Invitrogen	Cat#17-7321-81 RRID:AB_469507	FACS (1 μl per test)IF (1:100)
Antibody	Anti-IL-6 (mouse monoclonal)	Biolegend	Cat#504507 RRID:AB_10694868	FACS (1 μl per test)IF (1:100)
Antibody	Anti-BrdU (mouse monoclonal)	eBioscience	Cat#11-5071-42 RRID:AB_11042627	FACS (1 μl per test)
Antibody	Anti-Endomucin (mouse polyclonal)	R&D	Cat#AF4666	IF (1:100)
Antibody	Anti-CD150 (mouse monoclonal)	Biolegend	Cat#115908 RRID:AB_345278	IF (1:100)
Antibody	Anti-Lineage Panel (mouse monoclonal)	Biolegend	Cat#133307 RRID:AB_11124348	IF (1:100)
Antibody	Anti-Goat AF488 (goat polyclonal)	Invitrogen	Cat#A32814 RRID:AB_2762838	IF (1:1000)
Antibody	Anti-TNF-alpha (mouse monoclonal)	Sino Biological	Cat#50349-R023	2 μg ml^–1
Antibody	Anti-Rabbit AF488 (rabbit polyclonal)	Invitrogen	Cat#R37118 RRID:AB_2556546	IF (1:1000)
Antibody	Anti-OVA257-264 (SIINFEKL) peptide bound to H-2K^b (mouse monoclonal)	Invitrogen	Cat#17-5743-82 RRID:AB_1311286	FACS (1 μl per test)
Antibody	Anti-IL-2 (mouse monoclonal)	eBioscience	Cat#25-7021-80 RRID:AB_1235007	FACS (1 μl per test)
Chemical compound, drug	Diphtheria toxin (DT)	Sigma-Aldrich	Cat#D0564-1MG	40 μg kg^–1 body mass
Chemical compound, drug	BrdU (5-Bromo-2´-Deoxyuridine)	Sigma-Aldrich	Cat#B5002-250mg	125 mg kg^–1 body mass
Chemical compound, drug	CFSE (5-Carboxyfluorescein, Succinimidyl Ester)	Invitrogen	Cat#C2210	2.5 μM
Chemical compound, drug	GM-CSF	Abbkine	Cat#PRP2116	10 ng ml^–1
Chemical compound, drug	IL-4	novoprotein	Cat#CK15	10 ng ml^–1
Chemical compound, drug	Tamoxifen	Sigma-Aldrich	Cat#T5648	20 mg ml^–1 corn oil
Commercial kit	Chromium Single Cell 3′ GEM, Library & Gel Bead Kit v3	10 x Genomics	PN-1000075
Commercial kit	Chromium Chip B Single Cell Kit	10 x Genomics	PN-1000074
Cell line (Mus musculus)	NCTC clone 929	ATCC	CCL-1RRID:CVCL_0462
Cell line (Mus musculus)	B3Z hybridoma CD8 T cell	Dr. Nilabh Shastri
Other	scRNA sequencing data (raw and processed data)	This paper	GEO: GSE168224
Genetic reagent (Mus musculus)	C57BL/6 J	Shanghai Model Organisms
Genetic reagent (Mus musculus)	Tg(Pf4-icre)Q3Rsko/J (Pf4^Cre)	Jackson Laboratory	Stock No: 008535
Genetic reagent (Mus musculus)	Gt(ROSA) 26Sortm1(HBEGF) Awai/J (Rosa26^fs-iDTR)	Jackson Laboratory	Stock No: 007900
Genetic reagent (Mus musculus)	Gt(ROSA)26Sortm4(ACTB-tdTomato,-EGFP)Luo/J (Rosa26^fs-mTmG)	Jackson Laboratory	Stock No: 007576
Genetic reagent (Mus musculus)	Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J (Rosa26^fs-tdTomato)	Jackson Laboratory	Stock No: 007905
Genetic reagent (Mus musculus)	Scl^CreER mice	Göthert et al., 2005
Genetic reagent (Mus musculus)	Cxcl12tm2.1Sjm/J (Cxcl12^fs-DsRed)	Jackson Laboratory	Stock No: 022458
Genetic reagent (Mus musculus)	C57BL/6-Tg(TcraTcrb)1,100Mjb/J (OT-I)	Jackson Laboratory	Stock No: 003831
Strain, strain background (L. monocytogenes)	10403 S	Bishop and Hinrichs, 1987
Software, algorithm	Cell ranger_3.0.2	10 x Genomics	tenxRRID:SCR_01695
Software, algorithm	R_3.6.3	https://cran.r-project.org/	R 3.6.3
Software, algorithm	Seurat_3.0.2	Butler et al., 2018	SeuratRRID:SCR_016341
Software, algorithm	ggplot2_3.2.0	https://cran.r-project.org/web/packages/ggplot2/index.html	ggplot2 RRID:SCR_014601
Software, algorithm	clusterProfiler_3.12.0	Yu et al., 2012	clusterProfiler RRID:SCR_016884
Software, algorithm	pheatmap_1.0.12	https://cran.r-project.org/web/packages/pheatmap/	pheatmap RRID:SCR_016418
Software, algorithm	CellPhoneDB_2.1.7	Efremova et al., 2020	CellPhoneDB RRID:SCR_017054
Software, algorithm	CellChat_1.1.3	Jin et al., 2021	CellChat 1.1.3
Software, algorithm	symphony_1.0	Kang et al., 2021	symphony 1.0
Software, algorithm	MetaNeighbor_1.10.0	Crow et al., 2018	MetaNeighbor RRID:SCR_016727
Software, algorithm	iMAP_1.0.0	Wang et al., 2021b	iMAP 1.0.0
Software, algorithm	scmap_ 1.16.0	Kiselev et al., 2018	Scmap RRID:SCR_017338
Software, algorithm	enrichplot_1.4.0	Yu, 2019	enrichplot 1.4.0
Software, algorithm	Imaris_8.4	Bitplane	Imaris RRID:SCR_007370
Software, algorithm	FlowJo_10	BD Bioscience	FlowJo RRID:SCR_008520
Software, algorithm	ImageJ_ 1.8.0	National Institutes of Health	ImageJ RRID:SCR_003070
Other	DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride)	Thermo Fisher	Cat#D1306	IF (0.5 µg/mL)
Other	Corn oil	Sigma-Aldrich	Cat#PHR2897	Tamoxifen dissolution
Other	Lymphocyte Separation Medium	TBD Science	Cat#LTS1077	Liver cell isolation

83 in total

1. Expression of interleukin (IL) 1 type I and type II receptors in megakaryocytic cells and enhancing effects of IL-1beta on megakaryocytopoiesis and NF-E2 expression.

Authors: M Yang; K Li; C M Chui; P M Yuen; P K Chan; C K Chuen; C K Li; T F Fok
Journal: Br J Haematol Date: 2000-10 Impact factor: 6.998

2. Inflammation-Induced Emergency Megakaryopoiesis Driven by Hematopoietic Stem Cell-like Megakaryocyte Progenitors.

Authors: Simon Haas; Jenny Hansson; Daniel Klimmeck; Dirk Loeffler; Lars Velten; Hannah Uckelmann; Stephan Wurzer; Áine M Prendergast; Alexandra Schnell; Klaus Hexel; Rachel Santarella-Mellwig; Sandra Blaszkiewicz; Andrea Kuck; Hartmut Geiger; Michael D Milsom; Lars M Steinmetz; Timm Schroeder; Andreas Trumpp; Jeroen Krijgsveld; Marieke A G Essers
Journal: Cell Stem Cell Date: 2015-08-20 Impact factor: 24.633

3. CXCR4 is a key regulator of neutrophil release from the bone marrow under basal and stress granulopoiesis conditions.

Authors: Kyle J Eash; Jacquelyn M Means; Douglas W White; Daniel C Link
Journal: Blood Date: 2009-03-05 Impact factor: 22.113

4. Regulatory effects of TLR2 on megakaryocytic cell function.

Authors: Lea M Beaulieu; Elaine Lin; Kristine M Morin; Kahraman Tanriverdi; Jane E Freedman
Journal: Blood Date: 2011-03-31 Impact factor: 22.113

Review 5. Megakaryocyte development and platelet production.

Authors: Varda R Deutsch; Aaron Tomer
Journal: Br J Haematol Date: 2006-09 Impact factor: 6.998

Review 6. Megakaryocytes as immune cells.

Authors: Pierre Cunin; Peter A Nigrovic
Journal: J Leukoc Biol Date: 2019-01-15 Impact factor: 4.962

Review 7. Advances in megakaryocytopoiesis and thrombopoiesis: from bench to bedside.

Authors: Varda R Deutsch; Aaron Tomer
Journal: Br J Haematol Date: 2013-04-18 Impact factor: 6.998

8. Haematopoietic stem cells in perisinusoidal niches are protected from ageing.

Authors: Mehmet Saçma; Johannes Pospiech; Ruzhica Bogeska; Walter de Back; Jan-Philipp Mallm; Vadim Sakk; Karin Soller; Gina Marka; Angelika Vollmer; Rebekah Karns; Nina Cabezas-Wallscheid; Andreas Trumpp; Simón Méndez-Ferrer; Michael D Milsom; Medhanie A Mulaw; Hartmut Geiger; Maria Carolina Florian
Journal: Nat Cell Biol Date: 2019-11-04 Impact factor: 28.824