Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition

Oatley, Morgan; Bölükbası, Özge Vargel; Svensson, Valentine; Shvartsman, Maya; Ganter, Kerstin; Zirngibl, Katharina; Pavlovich, Polina V.; Milchevskaya, Vladislava; Foteva, Vladimira; Natarajan, Kedar N.; Baying, Bianka; Benes, Vladimir; Patil, Kiran R.; Teichmann, Sarah A.; Lancrin, Christophe

doi:10.1038/s41467-019-14171-5

Download PDF

Article
Open access
Published: 29 January 2020

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition

Nature Communications volume 11, Article number: 586 (2020) Cite this article

20k Accesses
57 Citations
29 Altmetric
Metrics details

Subjects

Abstract

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development.

Mapping human haematopoietic stem cells from haemogenic endothelium to birth

Article 13 April 2022

Vincenzo Calvanese, Sandra Capellera-Garcia, … Hanna K. A. Mikkola

Single-cell transcriptome analysis of embryonic and adult endothelial cells allows to rank the hemogenic potential of post-natal endothelium

Article Open access 16 July 2022

Artem Adamov, Yasmin Natalia Serina Secanechia & Christophe Lancrin

Embryonic endothelial evolution towards first hematopoietic stem cells revealed by single-cell transcriptomic and functional analyses

Article Open access 20 March 2020

Siyuan Hou, Zongcheng Li, … Yu Lan

Introduction

Understanding the developmental origin of haematopoietic stem and progenitor cells (HSPCs) is of critical importance to efforts to produce blood and blood products in vitro for medical applications. HSPCs originate from endothelial cells in the aorta-gonad-mesonephros (AGMs) of mid-gestation embryos^1,2. This process known as the endothelial to haematopoietic transition (EHT) requires drastic morphological changes that have been directly visualized through time-lapse imaging studies both in vitro and in vivo^3,4,5,6. EHT is a highly conserved process that has been studied across vertebrate models from Xenopus and zebrafish to mice⁷. Importantly, the human definitive blood system also has an endothelial origin⁸.

The best tools so far to detect endothelial cells with haemogenic capabilities rely on using fluorescent reporters under the control of Runx1⁹ or Gfi1¹⁰ regulatory elements, two key transcription factors in the process of EHT. Cells expressing these transcription factors already co-express blood and endothelial genes. However, we still do not know the nature of the endothelial cells that will acquire the expression of these transcription factors. Nor do we know whether any endothelial cell in the AGM can initiate the haematopoietic programme or if a certain type of endothelial cell is primed to undergo EHT.

Despite our lack of characterization of the definitive precursor to HSPC development, haemogenic endothelium (HE), recent advances have been made in terms of in vitro HSPC generation via an endothelial intermediate. Through the use of transcription factor cocktails, both human pluripotent stem cells via a HE stage and adult mouse endothelial cells have been successfully reprogrammed into multi-potent, definitive haematopoietic stem cells (HSCs)^11,12. However, the use of endothelial populations in the process emphasizes the importance of an improved understanding of HE.

The early haematopoietic hierarchy has been described as a three-step process based on phenotypic characteristics. Specifically, Pro-HSC, Pre-HSC type I and type II populations have been defined based on their expression of the cell surface markers CD41, CD43 and CD45, as well as the time taken to mature into definitive HSCs in OP9 co-culture¹³. Recently, an in-depth transcriptional investigation was performed on the type I and type II Pre-HSC populations at day 11 of mouse embryonic development; however, the earlier stages of EHT remain largely uncharacterized¹⁴. Indeed, gaining a solid understanding of HE and the initial steps that endothelial cells must take to become HSPCs has proved difficult in the absence of a robust marker.

Through antibody screening and single-cell RNA sequencing (sc-RNA-seq), we identified CD44 as a marker of EHT, enabling the isolation of key cellular stages of blood cell formation in the embryonic vasculature. CD44 is a cell surface receptor principally involved in the binding of the extracellular matrix molecule hyaluronan¹⁵. Its cell surface expression has been used to identify cancer stem cell populations and has been strongly linked to the metastatic potential of many cancers^{16,17,18,19,20}. Although previous research has revealed the importance of CD44 in HSPC migration to the bone marrow, the role of the receptor in early embryonic haematopoiesis has not been characterized²¹. Using CD44 expression in conjunction with VE-Cadherin (VE-Cad) and Kit, we are able to clearly differentiate between the different types of AGM endothelial cells, Pre-HSPC-I and Pre-HSPC-II more accurately than using the combination of VE-Cad, CD41, CD43 and CD45 markers. This has allowed us to perform extensive transcriptional profiling, making it possible to characterize the very earliest changes in haematopoietic differentiation from endothelial cells. Moreover, by disrupting the interaction of CD44 and its ligand, we could inhibit EHT, demonstrating an unexpected role for CD44 in the emergence of HSPCs.

Results

CD44 is a potential marker of early haematopoietic fate

HSPCs have an endothelial origin within the embryo^4,5,6,22. To better characterize the EHT, we performed both an in vitro antibody screen and an in vivo sc-RNA-seq experiment to identify markers allowing the identification of subpopulations within VE-Cad⁺ endothelial cells (Supplementary Fig. 1). Antibodies against 176 cell surface markers²³ were tested against the VE-Cad⁺ population generated from the in vitro ESC differentiation system into blood cells²⁴. Forty-two of these markers were expressed on VE-Cad⁺ cells (Supplementary Table 1). We looked for bimodal expression to separate distinct endothelial populations and identified a shortlist of 16 candidates to test in vivo, including CD41 and Kit, already known to split the VE-Cad⁺ cells¹³. Six of these markers (CD44, CD51, CD61, CD93, MadCam1 and Sca1) were found to split the VE-Cad⁺ endothelial population of the AGM in two (Fig. 1a and Supplementary Fig. 2). In parallel, we used sc-RNA-seq to analyse the transcriptional profiles of VE-Cad⁺ cells from the AGM region at E10.5 (Fig. 1). Clustering analysis identified a population with both haematopoietic and endothelial gene expression, distinct from the other endothelial population (Fig. 1b and Supplementary Data 1). Bioinformatics analysis showed that Cd44 is one of the best marker genes for this population of transitioning cells co-expressing endothelial and haematopoietic genes (Fig. 1c). The expression of Cd44 was also positively correlated with other known haematopoietic markers such as Runx1, Gfi1 and Adgrg1 (Gpr56) (Fig. 1d). Given the association of Cd44 with endothelial cells undergoing EHT at both the protein and mRNA level, we decided to further investigate its role in embryonic haematopoiesis.

**Fig. 1: Search for markers to dissect the endothelial to hematopoietic transition.**

CD44 marks different cell populations in the AGM

To validate our screening results and investigate the identity of CD44⁺ cells, we performed immunofluorescence and more detailed flow cytometry analysis on the AGM region of mouse embryos (Fig. 2). Immunofluorescence of cross-sections of mouse AGMs revealed that CD44 marked cells that were part of the vascular wall and cells that were incorporated in haematopoietic clusters at E10 and E11 (Fig. 2a and Supplementary Fig. 3). Different levels of CD44 expression could be noticed including some parts of the arterial wall being negative for this marker (Supplementary Fig. 3). Flow cytometry revealed that CD44 expression significantly increased in the VE-cad⁺ endothelium of the AGM between E9.5 and E10.5 when cells are undergoing EHT (Fig. 2b, c). Furthermore, by staining with an antibody against Kit (a marker of intra-aortic haematopoietic clusters)²⁵, we found that a large proportion of cells with lower levels of CD44 expressed little or no Kit (Fig. 2d).

**Fig. 2: CD44 splits the VE-Cadherin⁺ cells of the AGM into different populations.**

By grouping the cells based on their expression of CD44 and Kit, we found these populations to be significantly different in terms of cell size, suggestive of cells undergoing a morphological transition (Fig. 2e). Altogether, our results indicate that CD44 marks a subset of endothelial cells and cells in the haematopoietic clusters of the AGM during the key window of HSPCs development in the mouse embryo.

Detection of two CD44⁺ populations expressing blood genes

Using the Biomark HD single-cell quantitative PCR (qPCR) platform, we analysed the expression of 95 genes associated with both endothelial and haematopoietic cell types²⁴. We performed extensive transcriptional profiling on the CD44^Neg, CD44^LowKit^Neg, CD44^LowKit^Pos and CD44^High populations identified (Fig. 2d) between E9.5 and E11.5 (342 cells in total). We found that the CD44^LowKit^Pos and CD44^High populations expressed numerous haematopoietic genes (Supplementary Fig. 4 and Supplementary Data 2). The CD44^LowKit^Pos cells also expressed many endothelial genes as in our sc-RNA-seq analysis (Fig. 1d); however, the CD44^High cells appeared to be more advanced in the EHT process and lacked endothelial gene expression (Supplementary Fig. 4). Of note, the CD44^LowKit^Pos population expressed specifically Gfi1 and Itgb3, as well as Gata2, Runx1, Lyl1, Erg, Fli1, Lmo2 and Tal1, whose simultaneous co-expression is responsible for the dual endothelial-haematopoietic identity of pre-HSPCs (Supplementary Fig. 4)²⁴.

Conversely, both the CD44^Neg and CD44^LowKit^Neg populations showed specific endothelial gene signatures and lacked haematopoietic gene expression (Supplementary Fig. 4). Despite their different CD44 expression patterns, these cell populations clustered together (Supplementary Fig. 4). We repeated this experiment using another selection of 96 genes based on our sc-RNA-seq experiment (Supplementary Table 2 and Supplementary Fig. 5). With this gene list, we were able to confirm the dual endothelial-haematopoietic and haematopoietic identities of the CD44^LowKit^Pos and CD44^High populations, respectively. Surprisingly, the CD44^Neg and CD44^LowKit^Neg populations formed two distinct clusters (Supplementary Fig. 5b, Fig. 3a, b and Supplementary Data 3). In addition to Cd44, we found the genes Adgrg6, Fbn1, Pde3a, Smad6, Smad7 and Sox6 to be upregulated in the CD44^LowKit^Neg cells compared with CD44^Neg. In contrast, Bmp4, Kit, Hmmr and Pde2a were more expressed in CD44^Neg endothelial cells (Supplementary Fig. 5b).

**Fig. 3: The VE-Cad⁺ subpopulations defined by CD44 are transcriptionally distinct.**

Although the four groups defined by VE-Cad, CD44 and Kit were confirmed to be distinct transcriptionally; our clustering analysis found a fifth population (SC_3) composed of cells from both CD44^LowKit^Neg and CD44^LowKit^Pos groups (Supplementary Fig. 5b and Fig. 3a). Its transcriptional profile was found to be intermediary between SC_2 (CD44^LowKit^Neg) and SC_4 (CD44^LowKit^Pos), e.g., it expressed Adgrg1, Runx1, Itgb3 and Spi1 such as SC_4 but still expressed Adgrg6 and Pcdh12 such as SC_2 (Supplementary Fig. 5b). Interestingly, it is within this transitional SC_3 population that we saw the upregulation of Runx1, Spi1 and Gfi1, which are three of the four transcription factors used to reprogramme adult mouse endothelial cells into HSCs¹².

In conclusion, single-cell quantitative reverse transcriptase PCR (q-RT-PCR) analysis identified three homogeneous VE-Cad^Pos CD44^Pos populations (CD44^LowKit^Neg, CD44^LowKit^Pos and CD44^High) with an increasing haematopoietic gene expression and decreasing endothelial gene transcription. This would suggest a developmental link between the CD44^Low populations where CD44^LowKit^Neg cells would be the direct precursors of the CD44^LowKit^Pos population, which would then go on to generate CD44^High cells.

Pro-HSC, Pre-HSC-I and Pre-HSC-II populations express CD44

To place our results in the context of known AGM endothelial subpopulations, we performed transcriptional analysis of the Pro-HSC, Pre-HSC type I and Pre-HSC type II populations defined by the combination of VE-cad, CD41, CD43 and CD45 markers (Supplementary Fig. 6a and Fig. 4a)¹³. Gating used in Fig. 4a was determined based on fluorescence minus one controls and reflects the conditions we used to sort these rare haematopoietic populations. Although the gates were not perfect, they were chosen to ensure we could capture the necessary number of cells for analysis. From a sample of 0.835 × 10⁶ cells we analysed, there were 168 Pro-HSCs, 244 Pre-HSCs type I and 708 Pre-HSCs type II observed. Given these low cell numbers, it was not practical to use more stringent gating.

**Fig. 4: Comparison of the CD44 populations with Pro-HSC, Pre-HSC type I and type II.**

Interestingly, all populations were CD44 positive (Fig. 4b and Supplementary Fig. 6b). Following hierarchical clustering, we found three clusters: the first mostly composed of Pro-HSCs, a second being a mix of Pro-HSCs and Pre-HSCs type I, and a third one composed only of Pre-HSCs type II (Supplementary Fig. 7 and Fig. 4c). We then performed a clustering analysis in conjunction with the populations defined by CD44 (Fig. 4d). This revealed that the SC_5 (CD44^High) population closely associated with the Pre-HSC type II and the SC_4 (CD44^LowKit^Pos) population with Pre-HSC type I and part of the Pro-HSCs. SC_2 (CD44^LowKit^Neg) clustered closely with the remaining Pro-HSCs. Finally, only five cells with Pre-HSCs type I and Pro-HSCs phenotype clustered with the SC_1 (CD44^Neg). Ninety-seven per cent of Pro-HSCs, Pre-HSCs type I and Pre-HSCs type II were CD44 positive (Supplementary Fig. 7a and Supplementary Data 4). Overall, we have demonstrated that the phenotypes based on CD44 expression in combination with Kit could allow us to isolate all key populations in the process of HSCs formation more accurately than the phenotypes previously described.

CD44^Neg and CD44^LowKit^Neg are distinct transcriptionally

To further compare the two endothelial populations found in the AGM, we performed RNA-seq on 25-cell bulk samples from these populations across 3 different time points (E9.5, E10 and E11). We analysed as well the more advanced stages in EHT: CD44^LowKit^Pos at E9.5 and E10, and CD44^High at E11. The bulk RNA-seq approach allowed us to detect low abundant genes (such as genes encoding transcription factors) more efficiently than sc-RNA-seq and also to measure smaller changes in gene expression between populations.

The samples clustered according to their marker expression, despite the difference in developmental time, confirming our previous experiments (Fig. 5a and Supplementary Data 5). We identified several haematopoietic genes switched on in the CD44^LowKit^Neg population, including Ctsc, Nfe2, Runx1 and Ifitm1, suggesting that these cells were subjected to the EHT process (Fig. 5b). The expression pattern of endothelial genes fits with our previous observations—more highly expressed in CD44^Neg and CD44^LowKit^Neg, moderately expressed in CD44^LowKit^Pos and absent in CD44^High (Fig. 5b). Moreover, we used this dataset to check the expression of genes corresponding to proteins we found in our antibody screen (Fig. 1a). Cd93 and Madcam1 showed an endothelial expression pattern and could be used to separate endothelial from blood cells in the AGM. In contrast, Itgb3 marks specifically the CD44^LowKit^Pos population, whereas Ly6e marks both CD44^LowKit^Pos and CD44^High populations as shown previously in Fig. 3 and Supplementary Fig. 5.

**Fig. 5: Bulk RNA sequencing identifies early changes in the EHT process.**

Interestingly, this transcriptome analysis showed strong differences between the two endothelial populations of the AGM. We found 1605 genes differentially expressed between these two populations (p-value < 0.01, Wald’s test). Among them, several genes from the Notch pathway (Hey2, Jag1, Dll4, Hey1 and Notch1) were significantly more expressed in the CD44^LowKit^Neg population compared with CD44^Neg. Similarly, antagonists of the transforming growth factor-β (TGFβ)/bone morphogenetic protein (BMP) pathway, including Smad6, Smad7 and Bmper, were upregulated in CD44^LowKit^Neg cells compared with CD44^Neg. In contrast, target genes of the Wnt pathway (Lef1, Ccnd1 and Myc) were more highly expressed in CD44^Neg compared with CD44^LowKit^Neg. From this analysis we could also identify specific markers for each of the endothelial populations (Fig. 5c). Nr2f2, Pde2a, Aplnr and Kcne3 marked specifically CD44^Neg cells, whereas Adgrg6, Hey2, Akr1c14, Fas and Ltbp1 strongly marked the CD44^LowKit^Neg population.

The sc-RNA-seq performed by another group investigated the different stages of EHT in the AGM¹⁴, but were unable to identify the CD44^LowKit^Neg population. The gene expression pattern they obtained for the three other populations was very similar to the one we found with our bulk RNA analysis (Supplementary Fig. 8 and Supplementary Data 6). In addition, a recent study defined the HE in the AGM by the VE-Cad⁺ Gfi1⁺ Kit⁻ phenotype using the Gfi1:H2B-Tomato reporter mouse model¹⁰ and characterized it by sc-RNA-seq²⁶. This population matched with the CD44^LowKit^Neg cells. Indeed, it expressed Cd44, Hey2, Smad6, Smad7 and Adgrg6 but lacked the expression of CD44^Neg endothelial cell specific genes such as Pde2a, Aplnr, Nrp2 and Nr2f2 (Supplementary Fig. 9 and Supplementary Data 7).

Of note, a recently published mouse organogenesis sc-RNA-seq atlas²⁷ allowed us to find that Cd44 gene expression was restricted to a subset of arterial endothelial cells (co-expression of Gja5, Efnb2, Smad6 and Smad7) between E9.5 and E13.5 of mouse embryonic development (Supplementary Fig. 10) supporting our transcriptome results. In contrast, Cd44 expression is absent from most of adult mouse endothelial cells including arterial endothelium in the Tabula Muris sc-RNA-seq atlas²⁸ (Supplementary Fig. 11).

Overall, our results support the hypothesis that the CD44^Neg cells and the CD44^LowKit^Neg cells belong to two distinct endothelial populations. The CD44^Neg population expresses venous (Aplnr, Nr2f2 and Nrp2) and arterial (Sox17, Bmx and Efnb2) markers, whereas the CD44^LowKit^Neg has a clear arterial signature with stronger expression of Bmx, Jag1 and Hey2. Moreover, there are clear transcriptional links between the two CD44^Low populations and the initiation of several blood markers already at the CD44^LowKit^Neg stage suggests that this population is in fact the endothelial precursor of CD44^LowKit^Pos and CD44^High cells, and hence of haematopoietic development.

CD44^LowKit^Neg endothelial cells are quiescent

A large number of the differentially expressed genes between the two endothelial populations belonged to metabolic processes (395 out of 1605 genes; 1.32-fold enrichment, p-value < 0.05, Fisher’s exact test). We therefore identified specific metabolic pathways distinguishing the two populations (Fig. 6a, b and Supplementary Data 8). Notably, the CD44^LowKit^Neg population showed a pronounced downregulation of genes coding for enzymes involved in glycolysis, the TCA cycle and respiration, suggesting reduced ATP generation. Furthermore, amino acid and nucleotide biosynthesis genes were also downregulated. Altogether, it suggests that the CD44^LowKit^Neg is in a non-proliferative, metabolically quiescent state, which is in line with the smaller size of this population compared with the CD44^Neg (Fig. 2e). To test this hypothesis, we performed a cell cycle analysis of the four different CD44 populations at E10 and we found that indeed CD44^LowKit^Neg population was the most quiescent compared with the other endothelial subset (Fig. 6c and Supplementary Fig. 12). Interestingly, the proliferation rate increased, as the cells were progressing through EHT consistent with the observations made by another group²⁹.

**Fig. 6: Reduced expression of genes involved in TCA cycle and glycolysis in CD44^LowKit^Neg endothelial cells.**

Given that endothelial cells are known to obtain most of their energy from glycolysis³⁰, this change in metabolic status suggests a loss of endothelial identity. These cells also show a marked increase in the expression of pathways leading to lipids with regulatory function: glycerolipids, glycerophospholipids, phosphatidylinositol and sphingolipids (Supplementary Fig. 13a).

Moreover, we observed that several genes involved in autophagy, a process known to be regulated by phosphatidylinositol and sphingolipids^31,32,33,34, were also highly upregulated in the CD44^LowKit^Neg population (Supplementary Fig. 13b). Concordantly, two key processes accompanying autophagy, ubiquitylation and proteolysis, were also upregulated. As autophagy has been shown to play a key role in embryonic development and haematopoiesis^35,36,37, this provides further support to the CD44^LowKit^Neg cells being in transit from endothelial to haematopoietic identity.

Runx1 is dispensable for the formation of CD44^LowKit^Neg

CD44 has allowed us to clearly define the key VE-Cad⁺ populations in the AGM. The transcription factor RUNX1 is essential for HSPC formation and is known to downregulate endothelial identity through its target genes Gfi1 and Gfi1b^10,38. Next, we decided to evaluate the impact of Runx1 loss of function on the different CD44⁺ cells. Using a Runx1 knockout mouse model³⁹, we stained for VE-Cad, CD44 and Kit expression, and performed transcriptional profiling on the sorted cells (Fig. 7, Supplementary Fig. 14 and Supplementary Data 9). We found that in the absence of Runx1 there is a loss of CD44^High and CD44^LowKit^Pos cells, and we observed a concomitant increase in the frequency of the CD44^LowKit^Neg population (Fig. 7a, b). Interestingly, we found no obvious transcriptional differences between the CD44^LowKit^Neg populations derived from Runx1^+/+ vs. Runx1^−/− embryos, indicating that RUNX1 is not necessary for the formation of these endothelial cells but for the promotion of the transition into CD44^LowKit^Pos cells (Supplementary Fig. 14).

**Fig. 7: *Runx1* is required for the generation of CD44^LowKit^Pos and CD44^High cells.**

VE-Cad^Pos CD44^Pos cells display haematopoietic potential

To understand the haematopoietic potential of the different populations defined by VE-Cad, CD44 and Kit expression, we performed ex vivo assays using an OP9 co-culture system. No colonies were formed at the single-cell level from either the CD44^LowKit^Neg or the CD44^Neg populations. However, by plating cells at a density of 300 cells per well, we could observe round cell colony formation from the CD44^LowKit^Neg population (4 out of 16 wells) but not from the CD44^Neg (0 out of 13 wells) (Fig. 8a). As this growth occurred 4 times out of a total of 16 wells, the frequency of cells giving rise to blood in CD44^LowKit^Neg population is <1 in 300, which is in line with the very low expression of haematopoietic genes in this population.

**Fig. 8: VE-Cad⁺ CD44⁺ populations have haematopoietic potential.**

In contrast, using single-cell sorting, we found that the CD44^LowKit^Pos population was the most potent with an average of 43% of single cells forming round cell colonies after 3 days of growth. Similarly, the CD44^High population produced haematopoietic colonies but with a lesser frequency; on average, 11% of single cells showed the ability to form haematopoietic colonies on OP9 (Fig. 8b, c). To uncover the differentiation potential of the cells generated by CD44^LowKit^Pos and CD44^High, we performed colony-forming unit (CFU) assays following the OP9 co-culture. Both populations readily generated both erythroid and myeloid colonies with the CD44^LowKit^Pos population demonstrating a significantly higher capacity than the CD44^High population (Fig. 8d, e). However, the fourfold increase in the number of CD44^LowKit^Pos colonies on OP9 did not correspond to a fourfold increase in CFU colonies, suggesting a higher replating efficiency of the CD44^High cells compared with CD44^LowKit^Pos (Fig. 8f). We further tested the lymphoid potential of the CD44^High population by growing cells for 21 days on either OP9 or OP9-DL1 with lymphoid-promoting cytokines. We demonstrated that this population could give rise to both B and T cells ex vivo (Fig. 8g and Supplementary Fig. 15).

Our transcriptome and fluorescence-activated cell sorting (FACS) analyses showed that CD44^LowKit^Pos were similar to the Pre-HSC type I, whereas the CD44^High were equivalent to Pre-HSC type II (VE-Cad^Pos CD45^Pos). Pre-HSC type I gives rise to type II in OP9 co-culture⁴⁰ and, as expected, by isolating CD44^LowKit^Pos cells, we could demonstrate that they were producing VE-Cad^Pos CD45^Pos on their way to produce blood cells supporting the differentiation relationship between the two CD44^Pos Kit^Pos populations (Supplementary Fig. 16).

Overall, we found that all populations expressing CD44 displayed the capacity to produce haematopoietic cells on OP9 cells including CD44^LowKit^Neg albeit at a very low frequency.

Interrupting hyaluronan binding to the CD44 reduces EHT

So far, we demonstrated that CD44 was a very useful marker to distinguish the different stages of EHT. Although the Cd44 knockout mice do not have a severe haematopoietic phenotype⁴¹, compensatory mechanisms through other Hyaluronan receptors (e.g., Hmmr⁴² expressed by some CD44^LowKit^Pos and CD44^High cells in Fig. 3a) may be at play to diminish the consequences of CD44 loss of function. To explore the functional role of CD44 in EHT, we employed a pharmacological approach. By treating CD44^High-sorted cells with a CD44 blocking antibody known to bind close to the hyaluronan binding site on the extracellular domain of CD44²⁹, we found that round cell colony formation could be inhibited in a dose dependent manner (Fig. 9a, b). The blocking antibody inhibited not only the number of colonies deriving from the ex vivo sorted cells but also the size of the colonies generated (Fig. 9c).

**Fig. 9: Blocking the interaction between CD44 and hyaluronan inhibits the EHT.**

To further investigate the role of CD44 in EHT, we used the in vitro ESC differentiation system mimicking embryonic haematopoiesis²⁴. We performed a haemangioblast culture and analysed CD44 expression at day 1, 2 and 3. Endothelial cells expressed CD44 at all tested time points (Fig. 9d).

We therefore applied the blocking antibody from the beginning of the culture and found that treatment with the antibody halved the number of HSPCs (VE-Cad⁻ CD41⁺) produced and significantly increased the percentage of endothelial cells in the culture (Fig. 9e, f). We could not get this effect using another CD44 antibody, which did not bind close to the hyaluronan binding site (Supplementary Fig. 17), suggesting the importance of CD44 interaction with its ligand for EHT. Given this result, we next attempted to manipulate the amount of hyaluronan in the culture. When 300 μg/mL of hyaluronidase was applied to the haemangioblast culture, we again observed a block in EHT characterized by a significant decrease in blood cell formation and an increase in the percentage of Pre-HSPCs (VE-Cad⁺ CD41⁺) (Fig. 9f). Using the methylumbelliferone (4MU) inhibitor, which blocks the synthesis of hyaluronan, we also observed the reduction of CD41⁺ cell number. Combining 4MU with hyaluronidase had a much more potent effect with a clear block in EHT at the Pre-HSPC stage.

In conclusion, these results demonstrate a regulatory role for hyaluronan and its receptor CD44 in the formation of HSPCs.

Discussion

Using antibody screening and sc-RNA-seq, we discovered that CD44 was a robust marker to distinguish all the main populations in the EHT process in the AGM. Combining CD44 with Kit and VE-Cad allowed us to discriminate the different stages of EHT more accurately than the method based on VE-Cad, CD41, CD43 and CD45 cell surface markers¹³. In addition, we showed that CD44 had an unexpected regulatory function in the EHT process.

Our work has been instrumental in distinguishing the different types of endothelial cells in the AGM region. Previously, Zhou et al.¹⁴ found only one type of AGM endothelial cells, identical to the CD44^Neg population we described in the present study (Supplementary Fig. 8). On the other hand, the study by Baron et al.²⁶ described two endothelial populations: non-HE and HE. Surprisingly, they had no more than 100 genes differentially expressed between them. In contrast, we found 1605 genes changing between the two AGM endothelial populations we identified. A close look at their E11 sc-RNA-seq data showed that their non-HE cells were heterogeneous. Seventy-three per cent of them were in fact HE cells with characteristics identical to the CD44^LowKit^Neg population, while the remaining cells formed a separate cluster of CD44^Neg cells (Supplementary Fig. 9). Therefore, our study is the first one to identify and compare thoroughly the different types of AGM endothelial cells. The CD44^LowKit^Neg cells have a gene expression signature strongly compatible with arterial identity (e.g., expression of Efbn2 and Sox17, and upregulation of Notch pathway target Hey2), while the CD44^Neg population co-expressed genes related to the venous (e.g., Nr2f2 and Aplnr) and arterial cell fates (e.g., Efnb2 and Sox17) (Fig. 5d). It is in line with previous sc-RNA-seq datasets from the AGM^14,26. In the first analysis by Zhou et al.¹⁴, 64% of CD44^Neg cells (18 out of 28) were Efnb2+Aplnr+, whereas 50% of them (14 out of 28) were Sox17+Aplnr+ (Supplementary Fig. 8). In the second dataset by Baron et al.²⁶, 36% of them (4 out of 11) were Efnb2+Aplnr+ and 18% (2 out of 11) were positive for Sox17 and Aplnr (Supplementary Fig. 9).

Of note, not all of the CD44^Neg cells co-expressed arterial and venous markers, suggesting heterogeneity within this population (Supplementary Figs. 8 and 9). This could be attributed to the fact that some of these endothelial cells come from the dorsal aorta (Supplementary Fig. 3) and the rest from the cardinal veins, which are entirely CD44^Neg (Supplementary Fig. 10). Indeed, cardinal veins are not removed when the AGM region is isolated, because they are very close to the aorta. It would explain the presence of endothelial cells expressing only venous markers within the CD44^Neg population.

Another interesting finding was the striking metabolic state difference between the two endothelial populations. The CD44^Neg cells appeared much more metabolically active than the CD44^LowKit^Neg, suggesting that the latter population is in a state of quiescence. This was further supported by our cell cycle analysis (Fig. 6). This is surprising given that the acquisition of a quiescent phenotype in endothelial cells occurs normally after birth following the completion of angiogenesis. FOXO1 has been described as an important transcription factor to induce quiescence in endothelial cells through suppression of Myc⁴³. Although we observe specific downregulation of Myc in CD44^LowKit^Neg cells, Foxo1 has a similar level of expression in the two endothelial populations (Supplementary Data 5). Recently, a study comparing lung endothelial cells between infant and adult mice showed that higher expression of SMAD6 and SMAD7 was linked with the induction of endothelial cell quiescence typical in adulthood⁴⁴. Interestingly, all the CD44^LowKit^Neg cells co-expressed Smad6 and Smad7 at the single-cell level, suggesting that the quiescent phenotype we observe could also be linked to the co-expression of the two inhibitory Smads.

The acquisition of quiescence in this context could be the first indication of haemogenic capacity. The metabolic features of the CD44^LowKit^Neg population were coupled with an increase in genes involved in autophagy. This is in line with the known role of autophagy in embryogenesis, haematopoiesis and stem cell maintenance^{35,36,37,45,46}, such as protein and organelle turnover and protection from reactive oxygen species. Our data thus suggest that these cells mark the resetting of metabolic and regulatory states to initiate the EHT process.

We consequently propose that the CD44^LowKit^Neg population is the source of HSPCs in the AGM (Fig. 10). Although it was suggested that HE cells in the human pluripotent stem cell differentiation model do not display arterial identity⁴⁷, our results clearly support the arterial source of HSPCs in the AGM. Of note, the CD44 protein has been detected in intra-aortic haematopoietic clusters during human embryogenesis⁴⁸, suggesting an evolutionary conservation of its expression pattern.

**Fig. 10: New model for the progression of EHT.**

Another characteristic for the initiation of the EHT is the inhibition of the BMP and TGFβ pathways as suggested by the increased expression of Smad6, Smad7 and Bmper in the CD44^Low Kit^Neg arterial population. Expression of RUNX1 in endothelial cells would then trigger haematopoietic gene expression. The dynamic interaction between the heptad of transcription factors GATA2, RUNX1, LYL1, ERG, FLI1, LMO2 and TAL1 co-expressed at the Pre-HSPC-I stage would eventually lead to the loss of endothelial gene expression²⁴ and give rise to the Pre-HSPC-II stage, cells expressing only haematopoietic genes.

Interestingly, we also detected CD44 on endothelial cells generated in the in vitro EHT model based on ESC differentiation. Of note, the analysis of the mouse organogenesis sc-RNA-seq atlas²⁷ showed that the Cd44 gene was mostly expressed by a fraction of arterial endothelial cells indicating that this gene has a restricted expression pattern during development (Supplementary Fig. 10). It made CD44 expression on endothelial cells in this in vitro model even more significant. In addition, we have shown that endothelial cells produced in vitro co-express the Smad6 and Smad7 genes as in the AGM CD44^Low Kit^Neg population^24,49. The production of blood progenitors in vitro is also going through an intermediary stage expressing endothelial, haematopoietic genes, the key transcription factors Erg, Fli1, Lmo2, Lyl1, Fli1, Tal1, Runx1 and Gfi1 as in the AGM²⁴. Finally, even though HSCs have not yet been generated from ESCs without the use of reprogramming, definitive erythroid, myeloid and lymphoid cells could be obtained from this model⁵⁰. These lineages could also be produced upon transplantation of in vitro derived progenitors in adult mice and could be detected up to 22 weeks after injection, indicating a long-term reconstitution⁵⁰. These elements convinced us that studying CD44 role in the in vitro haemangioblast culture would be relevant to AGM EHT.

Our detailed transcriptomics analysis of haemogenic and non-haemogenic endothelial cell populations in the AGM revealed the prerequisites needed by endothelial cells to generate HSPCs. Thus, our study may help to design protocols for the generation of HSCs ex vivo without the use of transcription factors, which still represents a considerable health risk. The manipulation of the interaction between CD44 and hyaluronan could offer a strategy for reprogramming endothelial cells into HSPCs.

In addition, our work could shed light on CD44 function in other cellular transitions such as the epithelial–mesenchymal transition occurring during metastasis²³. Given the role of CD44 in the metastatic process, it is possible that there is an overlap in its function for these transformations. Therefore, understanding the down-stream targets of the CD44–hyaluronan interaction could also have implications for cancer biology.

Methods

Timed mating and embryo dissection

For timed pregnancies, C57BL/6 wild-type mice or Runx1^+/− mice (two to three months old) were mated overnight. The welfare of adult mice used in this work was covered by the licence number 17/2019-PR approved by the Italian Health Ministry. Embryos were collected in phosphate-buffered saline (PBS) supplemented with 10% fetal bovine serum (FBS) (PAA Laboratories). The yolk sac of embryos derived from Runx1^+/− mice were genotyped using the Kappa Mouse Genotyping Kit (KAPA Biosystems) according to the manufacturer’s instructions. E9.5 to E11.5 embryos were staged based on somite counts. After removing the yolk sac, the AGM region was dissected by removing the head and tail above and below the limb buds then removing the limb buds, organs and somites by cutting ventrally and dorsally either side of the AGM.

All experiments were performed following the guidelines and regulations defined by the European and Italian legislations (Directive 2010/63/EU and DLGS 26/2014, respectively). They apply to foetal forms of mammals as from the last third of their normal development (from day 14 of gestation in the mouse). They do not cover experiments done with day 12 mouse embryos and at earlier stages. Therefore, no experimental protocol or license was necessary for the experiments performed on mouse embryos. Mice were bred and maintained at the EMBL Rome Animal Facility in accordance with European and Italian legislations (EU Directive 634/2010 and DLGS 26/2014, respectively).

C1 Fluidigm chip and single-cell RNA sequencing

AGM from E10 embryos were dissected and dissociated with collagenase (Sigma) at 37 °C for 30 min. The collagenase was deactivated with PBS supplemented with 10% FBS (Gibco). Cells were stained with anti-VE-Cad and anti-CD41 antibodies (Supplementary Table 3). VE-Cad⁺ cells from E10 embryos were isolated using FACS and mixed with Fluidigm suspension reagent (Fluidigm) in a 3:2 ratio. A primed Fluidigm C1 chip and 5 μl of cell suspension was loaded onto a C1 instrument. Cell capture was then assessed with a ×40 bright-field microscope and wells scored as single cell, doublet or debris. Lysis, reverse transcription and PCR reagents (Clontech Takara) along with ERCC spike-ins at 1 in 4000 dilution (Ambion) were added and mRNA-Seq RT & Amp script were performed overnight. The cDNA was then collected and diluted in Fluidigm C1 DNA dilution buffer.

Single-cell RNA sequencing data analysis

The paired 2 ×101 bp Illumina reads from the libraries were quantified using Salmon⁵¹ with the setting -l IU to indicate library topology and the optional flags–posBias and –gcBias, to account for coverage and amplification biases present in sc-RNA-seq protocols. As an index the cDNA annotation of Ensembl release 85 for GRCm38.p4 was used, together with ERCC spike-in sequences. The transcript per million (TPM) values were re-scaled to not include ERCC expression and only consider endogenous gene expression.

Technical features of the data were compared with manual annotation of samples in C1 chambers through microscopy. Samples with less than 500,000 mapped reads and >30% mitochondrial content were discarded from analysis. This left 78 single cells in the in vivo experiment.

To identify cells as endothelial or haematopoietic, expression levels of ten known markers were analysed (Cdh5, Kdr, Pecam1, Pcdh12, Sox7, Gfi1, Gfi1b, Myb, Runx1, Spi1). Cells were clustered using Principal Component Analysis (PCA) and a Gaussian Mixture Model with two components on these markers (Fig. 1b). A cluster of ten cells considered haematopoietic was identified (and annotated based on high Runx1 expression). Analysis was performed using the decomposition.PCA and mixture.GaussianMixture classes in the scikit-learn package. PCA analysis was performed on the log-transformed TPM values of the markers and the first two principal components were used for Gaussian Mixture.

Markers for hematopoietic cells were identified using a likelihood ratio test, where the alternative model included a binary term for whether the cell was haematopoietic, and the null model just assumed a common mean for all the cells. The P-values from the likelihood ratio test were corrected for multiple testing by the Bonferroni procedure. The top differentially expressed genes were investigated to find markers, which could be used in follow-up experiments, and Cd44 was considered a good candidate (Fig. 1c).

Analysis of the mouse single-cell transcriptome atlases

Seurat v.2 was used to perform sc-RNA-seq clustering analysis on the data retrieved from the Mouse Organogenesis Cell Atlas²⁷. We took the filtered data cds_cleaned.RDS from which the doublet and low-mRNA cells were removed. For annotation and metadata, the cell_annotation.csv table was used.

We selected the cells related to the Endothelial trajectory. For these files we created Seurat object and ran clustering analysis including normalization with LogNormalize method. Afterwards, we found 4890 variable genes using the following parameters: x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5. Next, we performed a scaling with linear model regressed on the number of UMI and clustering with dimensions 1:20 and resolution 1.0. With clustered data we generated t-SNE plots highlighting the genes we were interested in (Supplementary Fig. 10).

The Tabula Muris²⁸ Gene-count tables for FACS sorted adult Aorta, Brain, Diaphragm, Fat, Heart, Kidney, Limb, Liver, Lung, Mammary Gland, Pancreas and Trachea cells were downloaded from Figshare: https://figshare.com/articles/Single-cell_RNA-seq_data_from_Smart-seq2_sequencing_of_FACS_sorted_cells_v2_/5829687.

The expression matrix was then processed through the CONCLUS pipeline⁵²: https://github.com/lancrinlab/CONCLUS. We then generated t-SNE plots highlighting the expression pattern of Cdh5 and Cd44 genes (Supplementary Fig. 11).

In vitro ES cell differentiation system

The A2lox Empty embryonic stem (ES) cell line²⁴ was maintained and differentiated as follows. ES cells were maintained on mouse embryonic fibroblasts (MEFs) in KnockOut™ Dulbecco’s modified Eagle’s medium (DMEM) medium (Gibco, 10829018) supplemented with 1% Penicillin–Streptomycin (Gibco, 15070063), 1% l-glutamine (Gibco, 25030081), 1% MEM non-essential amino acids solution (Gibco, 11140050), 15% FBS (Gibco, 10270106), 0.024 μg mL⁻¹ of LIF (EMBL Protein expression and purification core facility) and 0.12 mM of 2-Mercaptoethanol (Gibco). Cells were maintained on MEFs and incubated at 37 °C with 5% CO₂ and 95% relative humidity. TrypLE™ Express Enzyme (1×) (Gibco, 12605036) was used to detach cells for passaging, collection or to create a single-cell suspension.

To begin differentiation into haemangioblast, the ESCs were passaged twice on 0.1% gelatin to remove MEFs first in DMEM-ES medium and then using IMDM-ES medium containing IMDM (Iscove’s modified Dulbecco medium) (Lonza; BE12-726F) supplemented with 1% Penicilliin–Streptomycin and 1% l-glutamine. In addition, 15% FBS (Gibco, 10270106), 0.024 μg/mL LIF and 0.12 mM β-mercaptoethanol were added. Once MEFs were removed, cells were collected and cultured in untreated 10 cm² petri dishes at a density of 0.3 × 10⁶ cells per dish with EB medium containing IMDM (supplemented with 1% Penicillin–Streptomycin and 1% l-glutamine), 10% FBS (Gibco, 10270106), 0.6% Transferrin (Roche, 10652), 0.03% monothioglycerol (MTG) (Sigma, M6145) and 50 μg/mL ascorbic acid (Sigma, A4544). After 3 days in culture, Flk1⁺ haemangioblast cells were isolated through magnetic activated cell sorting²⁴ using an anti-Flk1 APC conjugated antibody (Supplementary Table 3) and anti-APC microbeads (Miltenyi Biotec, 130-090-855).

For the haemangioblast differentiation, Flk1⁺ cells were cultured on gelatin for 24 to 72 h in IMDM medium supplemented with 1% Penicillin–Streptomycin, 1% l-glutamine, 15% FBS (Gibco, 10270106), transferrin (Roche), MTG (Sigma) and 50 mg/μL of ascorbic acid (Sigma), 15% D4T²⁴, 10 μg/mL VEGF (Preprotech, 500-P131) and 10 μg/mL IL6 (Preprotech, 216-16). The cells could then be collected with TrypLE express and cell populations analysed by flow cytometry using anti-VE-Cad, anti-CD41 and anti-Kit antibodies (Supplementary Table 3). For in vitro CD44–hyaluronan interaction experiments Flk1⁺ cells were plated on gelatin, as per the haemangioblast differentiation protocol, and incubated with either 10 μg/mL anti-CD44 antibody [KM201] (Supplementary Table 3), 300 μg/mL of hyaluronidase (Sigma, H4272) and 500 μM of 4MU (Sigma, M1381-25G). Generation of haematopoietic and endothelial populations was assessed by flow cytometry after 48 h in culture.

Antibody screen, flow cytometry and cell sorting

The antibody screen was performed using the Mouse Cell Surface Marker Screening Panel (BD Bioscience; Supplementary Table 3) according to the manufacturer’s instructions. Cells from haemangioblast culture and dissociated AGMs were stained with different combination of antibodies (Supplementary Table 3). Dyes 7-AAD (Invitrogen, A1310) or Sytox Blue (Invitrogen, S34857) were used to exclude dead cells. FACS analysis was done using FACS Aria III (Becton Dickinson) and FACS Diva software. Data were later analysed using FlowJo v10.1r5 (Tree Star, Inc.). For single-cell qPCR analysis, cells were sorted using an 85 μm nozzle directly into the reaction buffer (Cells Direct qRT-PCR Kit, Invitrogen) and were snap frozen. For OP9 co-culturing assays, cells were sorted using 100 μm nozzle directly into a 96-well culture dish (Costar).

Immunofluorescence and confocal microscopy

Mid-gestation embryos were dissected and fixed in 4% paraformaldehyde for 15 min at room temperature then incubated in 15% sucrose solution for 1 h before freezing in OCT (Tissue-Tek). Ten-micrometer transverse cryo-sections of the AGM region were then placed on superfrost plus slides (Thermo Scientific). Sections were washed in PBS, incubated in 1 M glycine solution and permeabilized with 0.3% Triton X-100 (Sigma). Blocking solution consisting of 5% donkey serum, 5% goat serum and 0.1% Tween-20 (Sigma) in tris-buffered saline (TBS) buffer was applied to sections for 2 h at room temperature. Sections were incubated in primary antibodies overnight at 4 °C and then washed. Secondary antibodies (Thermo Scientific; Supplementary Table 3) were applied for 1 h at room temperature and were washed before 4′,6-diamidino-2-phenylindole nuclear stain (Invitrogen) was applied for 15 min. Slides were washed before being mounted with Prolong gold (Life Technologies) and imaged on a Leica SP5 confocal microscope.

Single-cell q-RT-PCR

Single cells were sorted directly into lysis buffer and snap frozen. Samples were reverse transcribed with superscript III reverse transcriptase from the Cells Direct one-step qRT-PCR Kit (Invitrogen) for 15 min at 50 °C. The cDNA was then pre-amplified for 20 cycles with 25 nM final concentration of each outer primer for a set of 96 target genes (Supplementary Table 2). The cDNA was then diluted with loading reagent (Fluidigm) and SoFastTM EvaGreen supermix (Biorad), and were loaded onto a chip with inner primer mix. Amplification of the target genes was measured with the Fluidigm Biomark HD system, with the Biomark Data Collection software and the GE96 × 96 + Meltv2.pcl programme.

Single-cell qPCR data analysis

Initial analysis of single-cell qPCR data were performed using the Fluidigm Real Time PCR analysis software²⁴. Hierarchical clustering and PCA analysis were performed using the SINGuLAR analysis toolkit (Fluidigm version 3.5) in R software (version 3.2.1).

Bulk RNA sequencing and data analysis

Bulk RNA-seq was performed as described by the SmartSeq2 protocol⁵³. Briefly, 25 cells from each group (Supplementary Table 4) were FACS sorted directly into lysis buffer containing 0.2% Triton X-100, oligo-dT primers and dNTP mix, and then snap frozen. Reverse transcription was then performed, followed by pre-amplification for 14 cycles. Nextera libraries were then prepared and sequenced on the Illumina Next Seq sequencer.

Sequencing data were analysed with the aid of the EMBL Galaxy tools (galaxy.embl.de)⁵⁴—specifically FASTX for adaptor clipping, RNA STAR for mapping and htseq-count for obtaining raw gene expression counts. The R software (version 3.3.1, http://www.R-project.org) was then used to generate heatmaps and tSNE plots using the DESeq2, Scater, Biobase and pHeatmap packages.

Cell cycle analysis

To analyse the cell cycle status of AGM-derived cells, we used the Click-iT plus EDU flow cytometry kit (Life Technologies; C10633). After dissection, AGMs were incubated in 10 μM of EDU in PBS with 10% FBS for 1 h at 37 °C. The tissue was then washed and incubated in 1.25 mg/mL of collagenase for 30 min. The sample was washed again and stained for cell surface markers (CD44, VE-Cad and Kit; Supplementary Table 3). Approximately 1000 cells were sorted based on cell surface expression into their different populations. Cells were then washed in PBS with 1% bovine serum albumin and then fixed in paraformaldehyde. The cells were permeabilized with a saponin-based wash solution before the addition of a copper protectant and AF488 picolyl azide for the detection of EDU. Finally, the cell populations were stained with Hoechst 33342 (Invitrogen; R37605) before re-analysis on the FACS Aria III, to determine the proportion of cells from each population in G0/G1, S and G2 phases.

OP9 co-culturing assays

OP9 cells (ATCC, CRL-2749) were maintained in MEM-alpha medium (Gibco, 12561056) with 20% FBS (ATCC, 30-2020). For limiting dilution, co-cultures cells were sorted directly onto a confluent OP9 stromal layer and incubated in medium conducive to haematopoietic development, IMDM (Lonza) treated with 5% penicillin–streptomycin (Gibco) and supplemented with 10% FBS (Gibco, 10270106), l-glutamine, transferrin, MTG, ascorbic acid, LIF, 50 ng/mL SCF (Preprotech, 250-03), 25 ng/mL IL3 (Preprotech, 213-13), 5 ng/mL IL11 (Preprotech, 220-11), 10 ng/mL IL6 (Peprotech), 10 ng/mL Oncostatin M (R&D Systems, 495-MO-025), 1 ng/mL bFGF (R&D Systems, 233-FB-025). Round cell colonies were quantified after 3–6 days in culture. For ex vivo CD44 blocking antibody experiments, 20 VE-Cad⁺CD44^High cells were sorted as per OP9 co-culturing assays, into a medium containing either no antibody, or 5 or 10 μg/mL of anti-CD44 antibody [KM201] (Supplementary Table 3). The number of colonies was assessed after 3–6 days in culture.

Haematopoietic colony-forming assay

One hundred cells were initially sorted onto a confluent OP9 stromal layer as per OP9 co-culturing assay. After 3 days in culture, cells were collected with TrypLE express (Gibco) and CFU-culture assays were initiated using Methocult complete medium (Stem Cell Technologies). Cells were grown in 35 mm culture dishes and colonies quantified after 7 days.

Lymphocyte progenitor assay

Fifty cells were sorted onto confluent OP9 (ATCC, CRL-2749) or OP9-DL1⁵⁵ stromal layers in MEM-alpha medium (Gibco) and supplemented with growth factors conducive to lymphocyte development, 20% FBS (ATCC, 30-2020), 50 ng/mL SCF (Peprotech), 5 ng/mL Flt-3L (Preprotech, 250-31L) and 1 ng/mL IL7 (Preprotech, 217-17). Medium was changed every 4–5 days and cells were split as necessary. Cells were cultured for 21 days before collecting, with TrypLE express for flow cytometry analysis.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The source data underlying Figs. 2a, e, 4b, 6c, 8c, f, 9b, c, f and Supplementary Fig. 17 are provided as a Source Data file.

All the expression data supporting the results reported in the article can be found in Supplementary Data 1–8. In addition, the sc-RNA-seq raw data are accessible from the ArrayExpress respository (E-MTAB-6987) and the bulk RNA-seq raw data were deposited at the NCBI Gene Expression Omnibus (GSE128971).

References

Medvinsky, A. & Dzierzak, E. Definitive hematopoiesis is autonomously initiated by the AGM region. Cell 86, 897–906 (1996).
Article CAS PubMed Google Scholar
de Bruijn, M. F. T. R. et al. Hematopoietic stem cells localize to the endothelial cell layer in the midgestation mouse aorta. Immunity 16, 673–683 (2002).
Article PubMed Google Scholar
Bertrand, J. Y. et al. Haematopoietic stem cells derive directly from aortic endothelium during development. Nature 464, 108–111 (2010).
Article ADS CAS PubMed PubMed Central Google Scholar
Boisset, J.-C. et al. In vivo imaging of haematopoietic cells emerging from the mouse aortic endothelium. Nature 464, 116–120 (2010).
Article ADS CAS PubMed Google Scholar
Kissa, K. & Herbomel, P. Blood stem cells emerge from aortic endothelium by a novel type of cell transition. Nature 464, 112–115 (2010).
Article ADS CAS PubMed Google Scholar
Lancrin, C. et al. The haemangioblast generates haematopoietic cells through a haemogenic endothelium stage. Nature 457, 892–895 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
Ciau-Uitz, A., Monteiro, R., Kirmizitas, A. & Patient, R. Developmental hematopoiesis: ontogeny, genetic programming and conservation. Exp. Hematol. 42, 669–683 (2014).
Article CAS PubMed Google Scholar
Oberlin, E., Tavian, M., Blazsek, I. & Peault, B. Blood-forming potential of vascular endothelium in the human embryo. Development 129, 4147–4157 (2002).
CAS PubMed Google Scholar
Swiers, G. et al. Early dynamic fate changes in haemogenic endothelium characterized at the single-cell level. Nat. Commun. 4, 1–10 (2014).
Google Scholar
Thambyrajah, R. et al. GFI1 proteins orchestrate the emergence of haematopoietic stem cells through recruitment of LSD1. Nat. Cell Biol. 18, 21–32 (2016).
Article CAS PubMed Google Scholar
Sugimura, R. et al. Haematopoietic stem and progenitor cells from human pluripotent stem cells. Nature 545, 432–438 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Lis, R. et al. Conversion of adult endothelium to immunocompetent haematopoietic stem cells. Nature 545, 439–445 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Rybtsov, S. et al. Tracing the origin of the HSC hierarchy reveals an SCF-dependent, IL-3-independent CD43(-) embryonic precursor. Stem Cell Rep. 3, 489–501 (2014).
Article CAS Google Scholar
Zhou, F. et al. Tracing haematopoietic stem cell formation at single-cell resolution. Nature 533, 487–492 (2016).
Article ADS CAS PubMed Google Scholar
Aruffo, A., Stamenkovic, I., Melnick, M., Underhill, C. B. & Seed, B. CD44 is the principal cell surface receptor for hyaluronate. Cell 61, 1303–1313 (1990).
Article CAS PubMed Google Scholar
Takaishi, S. et al. Identification of gastric cancer stem cells using the cell surface marker CD44. Stem Cells 27, 1006–1020 (2009).
Article CAS PubMed PubMed Central Google Scholar
Prince, M. E. et al. Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma. Proc. Natl Acad. Sci. USA 104, 973–978 (2007).
Article ADS CAS PubMed PubMed Central Google Scholar
Lopez, J. I. et al. CD44 attenuates metastatic invasion during breast cancer progression. Cancer Res. 65, 6755–6763 (2005).
Article CAS PubMed Google Scholar
Liu, C. et al. The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat. Med. 17, 211–215 (2011).
Article ADS CAS PubMed PubMed Central Google Scholar
Du, L. et al. CD44 is of functional importance for colorectal cancer stem cells. Clin. Cancer Res. 14, 6751–6760 (2008).
Article CAS PubMed Google Scholar
Avigdor, A. et al. CD44 and hyaluronic acid cooperate with SDF-1 in the trafficking of human CD34+ stem/progenitor cells to bone marrow. Blood 103, 2981–2989 (2004).
Article CAS PubMed Google Scholar
Zovein, A. C. et al. Fate tracing reveals the endothelial origin of hematopoietic stem cells. Cell Stem Cell 3, 625–636 (2008).
Article CAS PubMed PubMed Central Google Scholar
Pastushenko, I. et al. Identification of the tumour transition states occurring during EMT. Nature 556, 463–468 (2018).
Article ADS CAS PubMed Google Scholar
Bergiers, I. et al. Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis. eLife 7, https://doi.org/10.7554/eLife.29312 (2018).
Yokomizo, T., Ng, C. E. L., Osato, M. & Dzierzak, E. Three-dimensional imaging of whole midgestation murine embryos shows an intravascular localization for all hematopoietic clusters. Blood 117, 6132–6134 (2011).
Article CAS PubMed PubMed Central Google Scholar
Baron, C. S. et al. Single-cell transcriptomics reveal the dynamic of haematopoietic stem cell production in the aorta. Nat. Commun. 9, 2517 (2018).
Article ADS PubMed PubMed Central CAS Google Scholar
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Article ADS CAS PubMed PubMed Central Google Scholar
Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Article ADS CAS Google Scholar
Batsivari, A. et al. Understanding hematopoietic stem cell development through functional correlation of their proliferative status with the intra-aortic cluster architecture. Stem Cell Rep. 8, 1549–1562 (2017).
Article CAS Google Scholar
Quintero, M., Colombo, S. L., Godfrey, A. & Moncada, S. Mitochondria as signaling organelles in the vascular endothelium. Proc. Natl Acad. Sci. USA 103, 5379–5384 (2006).
Article ADS CAS PubMed PubMed Central Google Scholar
Yu, X., Long, Y. C. & Shen, H.-M. Differential regulatory functions of three classes of phosphatidylinositol and phosphoinositide 3-kinases in autophagy. Autophagy 11, 1711–1728 (2015).
Article CAS PubMed PubMed Central Google Scholar
Nascimbeni, A. C., Codogno, P. & Morel, E. Phosphatidylinositol-3-phosphate in the regulation of autophagy membrane dynamics. FEBS J. 284, 1267–1278 (2017).
Article CAS PubMed Google Scholar
Tan, X., Thapa, N., Liao, Y., Choi, S. & Anderson, R. A. PtdIns(4,5)P2 signaling regulates ATG14 and autophagy. Proc. Natl Acad. Sci. USA 113, 10896–10901 (2016).
Article CAS PubMed PubMed Central Google Scholar
Dall’Armi, C., Devereaux, K. A. & Di Paolo, G. The role of lipids in the control of autophagy. Curr. Biol. 23, R33–R45 (2013).
Article PubMed PubMed Central CAS Google Scholar
Riffelmacher, T. & Simon, A. K. Mechanistic roles of autophagy in hematopoietic differentiation. FEBS J. 284, 1008–1020 (2017).
Article CAS PubMed Google Scholar
Phadwal, K., Watson, A. S. & Simon, A. K. Tightrope act: autophagy in stem cell renewal, differentiation, proliferation, and aging. Cell. Mol. Life Sci. 70, 89–103 (2013).
Article CAS PubMed Google Scholar
Mizushima, N. & Levine, B. Autophagy in mammalian development and differentiation. Nat. Cell Biol. 12, 823–830 (2010).
Article CAS PubMed PubMed Central Google Scholar
Lancrin, C. et al. GFI1 and GFI1B control the loss of endothelial identity of hemogenic endothelium during hematopoietic commitment. Blood 120, 314–322 (2012).
Article CAS PubMed Google Scholar
Pinheiro, I. et al. Discovery of a new path for red blood cell generation in the mouse embryo. Preprint at https://www.biorxiv.org/content/10.1101/309054v1 (2018).
Rybtsov, S. et al. Hierarchical organization and early hematopoietic specification of the developing HSC lineage in the AGM region. J. Exp. Med. 208, 1305–1315 (2011).
Article CAS PubMed PubMed Central Google Scholar
Schmits, R. et al. CD44 regulates hematopoietic progenitor distribution, granuloma formation, and tumorigenicity. Blood 90, 2217–2233 (1997).
Article CAS PubMed Google Scholar
Nedvetzki, S. et al. RHAMM, a receptor for hyaluronan-mediated motility, compensates for CD44 in inflamed CD44-knockout mice: A different interpretation of redundancy. Proc. Natl Acad. Sci. USA 101, 18081–18086 (2004).
Article ADS CAS PubMed PubMed Central Google Scholar
Wilhelm, K. et al. FOXO1 couples metabolic activity and growth state in the vascular endothelium. Nature 529, 216–220 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Schlereth, K. et al. The transcriptomic and epigenetic map of vascular quiescence in the continuous lung endothelium. eLife 7, pii: e34423 https://doi.org/10.7554/eLife.34423. (2018).
Mortensen, M. et al. The autophagy protein Atg7 is essential for hematopoietic stem cell maintenance. J. Exp. Med. 208, 455–467 (2011).
Article CAS PubMed PubMed Central Google Scholar
Ho, T. T. et al. Autophagy maintains the metabolism and function of young and old stem cells. Nature 543, 205–210 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Ditadi, A. et al. Human definitive haemogenic endothelium and arterial vascular endothelium represent distinct lineages. Nat. Cell Biol. 17, 580–591 (2015).
Article CAS PubMed PubMed Central Google Scholar
Watt, S. M. et al. Functionally defined CD164 epitopes are expressed on CD34(+) cells throughout ontogeny but display distinct distribution patterns in adult hematopoietic and nonhematopoietic tissues. Blood 95, 3113–3124 (2000).
CAS PubMed Google Scholar
Shvartsman, M., Bilican, S. & Lancrin, C. Iron deficiency disrupts embryonic haematopoiesis but not the endothelial to haematopoietic transition. Sci. Rep. 9, 6414–12 (2019).
Article ADS PubMed PubMed Central CAS Google Scholar
Pearson, S., Cuvertino, S., Fleury, M., Lacaud, G. & Kouskoff, V. In vivo repopulating activity emerges at the onset of hematopoietic specification during embryonic stem cell differentiation. Stem Cell Rep. 4, 431–444 (2015).
Article CAS Google Scholar
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Article CAS PubMed PubMed Central Google Scholar
Shvartsman, M. et al. Single-cell atlas of major haematopoietic tissues sheds light on blood cell formation from embryonic endothelium. Preprint at https://www.biorxiv.org/content/10.1101/774547v1.full (2019).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Article CAS PubMed Google Scholar
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10 (2016).
Article CAS PubMed PubMed Central Google Scholar
Schmitt, T. M. & Zúñiga-Pflücker, J. C. Induction of T cell development from hematopoietic progenitor cells by delta-like-1 in vitro. Immunity 17, 749–756 (2002).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

We thank Kalina Stantcheva and Cora Chadick (EMBL Rome FACS Facility, Italy) for cell sorting; Andreas Buness (EMBL Rome Bioinformatics Services, Italy), Artem Adamov (EMBL, Italy) and Tallulah Andrews (Wellcome Trust Sanger Institute, UK) for help with bioinformatics analysis; Paul Collier (EMBL Genomics Core Facility, Germany) for bulk RNA-seq; Radvilė Prialgauskaitė (EMBL Rome) for help with immunofluorescence analysis; Gisela Luz (Patil Group, EMBL, Germany) for scientific illustration; Inke Näthke (University of Dundee, Scotland), Paul Heppenstall (EMBL, Italy) and Alexander Aulehla (EMBL, Germany) for fruitful scientific discussions. The European Molecular Biology Laboratory and the Wellcome Trust supported this work.

Author information

Özge Vargel Bölükbası
Present address: Stem Cell and Regenerative Biology Department, Harvard University, 7 Divinity Avenue, Cambridge, MA, 02138, USA
Valentine Svensson
Present address: Pachter Lab, Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA, USA
Polina V. Pavlovich
Present address: Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, D-79108, Freiburg, Germany
Vladislava Milchevskaya
Present address: Institut für Medizinische Statistik und Bioinformatik, Bachemer Strasse 86, 50931, Köln, Germany
Kedar N. Natarajan
Present address: Department of Biochemistry and Molecular Biology, The University of Southern Denmark, Danish Institute for Advanced Study, Campusvej 55, 5230, Odense M, Denmark
These authors contributed equally: Morgan Oatley, Özge Vargel Bölükbası.

Authors and Affiliations

European Molecular Biology Laboratory, EMBL Rome - Epigenetics and Neurobiology Unit, via E. Ramarini 32, 00015, Monterotondo, Italy
Morgan Oatley, Özge Vargel Bölükbası, Maya Shvartsman, Kerstin Ganter, Polina V. Pavlovich, Vladimira Foteva & Christophe Lancrin
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
Valentine Svensson, Kedar N. Natarajan & Sarah A. Teichmann
European Molecular Biology Laboratory, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
Valentine Svensson
European Molecular Biology Laboratory, Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117, Heidelberg, Germany
Katharina Zirngibl, Vladislava Milchevskaya & Kiran R. Patil
Moscow Institute of Physics and Technology, Institutskii Per. 9, Moscow Region, Dolgoprudny, 141700, Russia
Polina V. Pavlovich
European Molecular Biology Laboratory, Genomics Core Facility, Meyerhofstrasse 1, 69117, Heidelberg, Germany
Bianka Baying & Vladimir Benes

Authors

Morgan Oatley
View author publications
You can also search for this author in PubMed Google Scholar
Özge Vargel Bölükbası
View author publications
You can also search for this author in PubMed Google Scholar
Valentine Svensson
View author publications
You can also search for this author in PubMed Google Scholar
Maya Shvartsman
View author publications
You can also search for this author in PubMed Google Scholar
Kerstin Ganter
View author publications
You can also search for this author in PubMed Google Scholar
Katharina Zirngibl
View author publications
You can also search for this author in PubMed Google Scholar
Polina V. Pavlovich
View author publications
You can also search for this author in PubMed Google Scholar
Vladislava Milchevskaya
View author publications
You can also search for this author in PubMed Google Scholar
Vladimira Foteva
View author publications
You can also search for this author in PubMed Google Scholar
Kedar N. Natarajan
View author publications
You can also search for this author in PubMed Google Scholar
Bianka Baying
View author publications
You can also search for this author in PubMed Google Scholar
Vladimir Benes
View author publications
You can also search for this author in PubMed Google Scholar
Kiran R. Patil
View author publications
You can also search for this author in PubMed Google Scholar
Sarah A. Teichmann
View author publications
You can also search for this author in PubMed Google Scholar
Christophe Lancrin
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

M.O.: conceptualization, formal analysis, investigation, and visualization, writing—original draft, writing—review and editing. Ö.V.B.: conceptualization, formal analysis, investigation, visualization, writing—review and editing. V.S.: formal analysis, visualization, writing—review and editing. M.S.: investigation, formal analysis, writing—review and editing. K.G.: investigation, visualization, writing—review and editing. K.Z.: formal analysis, visualization, writing—review and editing. P.V.P.: formal analysis, writing—review and editing. V.M.: formal analysis, writing—review and editing. V.F.: investigation, writing—review and editing. K.N.N.: investigation, writing—review and editing. B.B.: investigation, writing—review and editing. V.B., supervision, writing—review and editing. K.R.P.: supervision, investigation, writing— review and editing. S.A.T.: supervision, investigation, writing—review and editing. C.L.: conceptualization, formal analysis, supervision, investigation, visualization, methodology, writing—original draft, project administration, writing—review and editing.

Corresponding author

Correspondence to Christophe Lancrin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review File

Reporting Summary

Description of Additional Supplementary Files

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Supplementary Data 5

Supplementary Data 6

Supplementary Data 7

Supplementary Data 8

Supplementary Data 9

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Oatley, M., Bölükbası, Ö.V., Svensson, V. et al. Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition. Nat Commun 11, 586 (2020). https://doi.org/10.1038/s41467-019-14171-5

Download citation

Received: 27 July 2018
Accepted: 18 December 2019
Published: 29 January 2020
DOI: https://doi.org/10.1038/s41467-019-14171-5

This article is cited by

Modelling post-implantation human development to yolk sac blood emergence
- Joshua Hislop
- Qi Song
- Mo R. Ebrahimkhani
Nature (2024)
CD32 captures committed haemogenic endothelial cells during human embryonic development
- Rebecca Scarfò
- Lauren N. Randolph
- Andrea Ditadi
Nature Cell Biology (2024)
Cross-species transcriptomics reveals bifurcation point during the arterial-to-hemogenic transition
- Shaokang Mo
- Kengyuan Qu
- Kuangyu Yen
Communications Biology (2023)
Meis1 establishes the pre-hemogenic endothelial state prior to Runx1 expression
- Patrick Coulombe
- Grace Cole
- Aly Karsan
Nature Communications (2023)
Integrative epigenomic and transcriptomic analysis reveals the requirement of JUNB for hematopoietic fate induction
- Xia Chen
- Peiliang Wang
- Jie Na
Nature Communications (2022)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.