Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine haemato-endothelial fate commitment

Domain: biology
Published: 2025-12-13
Editorial outcome: accept

Abstract
During development, blood generation begins in the yolk sac with the differentiation of haemato-endothelial mesoderm forming haematopoietic progenitors. This study aims to identify the crucial molecular regulators of haemato-endothelial mesoderm formation and to extend our knowledge of the process in an unbiased way. We employ a murine embryonic stem cell model that recapitulates embryonic blood development, and perform targeted CRISPR-Cas9 knockout screens focusing on transcription factors and chromatin regulators. We identify the transcription factors ETV2, LDB1, SMAD1, SIX4 and ZBTB7b as regulators of haemato-endothelial mesoderm commitment. Embryonic stem cells lacking these regulators give rise to mesodermal subsets with a defined lineage differentiation bias, while transcriptome analysis of these cells uncovers the precise impact of each factor on gene expression in the developing mesoderm. Our study reveals molecular pathways governing mesodermal development crucial to allow endothelial and haematopoietic lineage specification and paves the way for future advances in haematopoietic stem cell applications.

Introduction
Lineage commitment is a complex and multi-factorial process that is achieved by the stepwise acquisition of cell fate changes, which begin in the pluripotent cells of the embryo. Within these cells, emergence of identity results from a combination of encoded genetic and structural chromatin-based information that ultimately determines individual gene expression programmes: while genetic information encodes transcription factors (TFs) that drive progenitor fate and differentiation, chromatin-based information shapes the accessibility of DNA to those TFs, providing a heritable chromatin landscape that can direct or reinforce lineage-specific transcriptional programmes.

Understanding the process by which blood cells are generated is particularly important. The comprehensive identification of factors that specify haematopoietic fate during mesodermal development not only provides valuable insights into lineage specification and reprogramming but is essential for the generation of haematopoietic progenitors from pluripotent stem cells for regenerative medicine. Accordingly, the regulation of the process of early haematopoietic specification in the embryo has been extensively studied in recent years. In mice, blood development initiates at around embryonic day 7 in the blood island of the yolk sac1,2. Here, blood cells develop from a mesodermal precursor with both haematopoietic and endothelial developmental potential: the haemato-endothelial mesoderm (HEM) or haemangioblast3–6. The HEM, in turn, initiates both primitive and definitive haematopoiesis by giving rise to primitive haematopoietic progenitors (pHPCs) and haemogenic endothelial cells (HECs), respectively7,8. pHPCs further differentiate to primitive blood cells, including primitive erythrocytes and primitive macrophages1,2,9. HECs initiate definitive blood development by undergoing an endothelial-to-haematopoietic transition (EHT), where, in a second wave of blood formation, mainly erythro-myeloid progenitors (EMPs) are formed10.

Upstream HEM develops from an early common mesoderm progenitor, called primitive mesoderm (priMes), that has a broad developmental potential for both the cardiac lineage and the haemato-endothelial lineage11,12. Therefore, the transition from priMes towards HEM is the first transition that locks a cell into a haemato-endothelial fate. Several transcription factors that affect mesodermal commitment towards a haemato-endothelial fate have been identified: Brachyury (T) and Eomesodermin (EOMES) effect changes to the chromatin that are crucial for the initial stages of pluripotent cell commitment towards a mesodermal fate13; while downstream, ETV2 is considered the master regulator of the transition of the multipotent priMes towards HEM. While loss of function of ETV2 results in a complete differentiation block of both lineages and early lethality14,15, ectopic expression of ETV2 enables fibroblasts to reprogram towards an endothelial or haematopoietic fate15–17. A recent study employing hypomorph ETV2 mutants highlighted a differential dose-dependency for lineage commitment, with haematopoietic lineage commitment being more sensitive to reduced ETV2 dosage than the endothelial lineage18. We also know which factors are further needed for full blood commitment after haemato-endothelial differentiation occurred, including TAL1, RUNX1, and GATA219. The major open question in the field today is how these—and possibly other yet-unidentified—regulators cooperate to allow the initial transition towards the blood lineage by the differentiation of priMes to the HEM.

Here, we answered these questions using a mouse embryonic stem cell (ESC) differentiation model that accurately recapitulates mesoderm development and embryonic haematopoiesis in the yolk sac7,20,21, combined with large-scale CRISPR-Cas9 KO screens focusing on transcription factors and chromatin regulators. We confirmed ETV2 as a master regulator driving primitive toward haemato-endothelial mesoderm commitment and identified several additional transcriptional regulators playing key roles in lineage specification and its repression, including LDB1, SMAD1, SIX4, and ZBTB7b, respectively. Loss-of-function studies of the newly identified core TFs revealed profound perturbations to normal haematopoiesis, further confirming the significance of our findings. Collectively, by employing large-scale CRISPR screening, we identify molecular regulators impacting on haematopoietic-endothelial lineage commitment.

Results
In depth characterisation of the developmental route to HEM
In vitro murine ESC differentiation recapitulates the stages of mesoderm differentiation to HEM—the precursor of blood and endothelial lineages—commitment in vivo21–23 (Supplementary Fig. 1a). Distinct mesodermal entities form during embryoid body (EB) culture and two surface markers, FLK1 (vascular endothelial growth factor receptor 2 (VEGFR2)) and PDGFRα (platelet-derived growth factor receptor alpha), distinguish distinct differentiation stages. FLK1 together with PDGFRα marks primitive mesoderm, a multipotent mesodermal population with a broader developmental potential (DP priMes), whereas haemato-endothelial mesoderm is single-positive for FLK1 (F_SP HEM). We first performed an in-depth phenotypic and molecular characterisation of the developing mesodermal populations to test and confirm the validity of this model. Whereas uncommitted cells do not express FLK1 or PDGFRa (DN), mesodermal commitment started at day 4 with the detection of P_SP cells and DP priMes; by day 5 all three types of mesoderm were present (P_SP, DP priMes, and F_SP HEM) (Fig. 1a, b). To confirm the developmental trajectory, we conducted gene expression analysis by single-cell RNA sequencing employing SORT-seq, which we then used to construct the developmental trajectory from DN → P_SP → DP priMes → F_SP HEM populations (Fig. 1c and Supplementary Fig. 1b, c). This analysis aligned with previous reports employing ESC differentiation models23,24 and with the kinetics of FLK1 and PDGFRα expression in our EB culture. To further validate the use of FLK1 and PDGFRα expression patterns to define mesodermal populations, we performed bulk RNA-seq on DN, P_SP, DP priMes, and F_SP HEM populations. Principal component analysis separated the four populations (Supplementary Fig. 1d) and allowed us to confirm the expression of relevant marker genes for distinct developmental stages: pluripotency genes (e.g. Nanog, Pou5f1, Fgf5) were expressed in DN cells, early mesodermal markers (e.g. Mesp1, Mesp2, Mixl1) in P_SP and DP priMes cells, and haematopoietic and endothelial genes (e.g. Lmo2, Tal1, Sox7) in F_SP HEM cells (Supplementary Fig. 1e).

As F_SP HEM is further differentiated towards the haemato-endothelial fate compared to the other mesodermal populations, F_SP HEM should display an increased developmental potential to form blood and endothelial lineages. To examine this, we isolated DN, P_SP, DP priMes, and F_SP HEM cells by FACS and placed them into haemangioblast (HB) culture to support their differentiation to haematopoietic and endothelial progenitors as well as vascular smooth muscle cells (VSM)6,25. After 2 days, we used CD41 and VE-cadherin (VE-cad) expression to distinguish haematopoietic (CD41+), endothelial (VE-cad+), pre-haematopoietic stem and progenitor cells (pre-HSPC, CD41+VE-cad+), and VSM cells (CD41-VE-cad-)26 by flow cytometry. As there is no specific surface marker for VSM cells suitable for flow cytometry, we will refer to CD41-VE-cad- cells as VSM-enriched, acknowledging the inferred nature of this identity. The HB culture experiments confirmed that DP priMes indeed had less capacity to form haematopoietic and endothelial lineages compared to F_SP HEM, which generate a higher frequency of CD41+ and VE-cad+ cells and fewer VSM-enriched cells (Fig. 1d, e). Accordingly, and as expected, DN and P_SP had a further reduced capacity to form haematopoietic and endothelial lineages compared to DP priMes/F_SP HEM (Fig. 1d, e). To examine the lineage potential of the two different mesodermal populations at higher resolution, we next established a high-dimensional flow cytometry panel using antibodies recognising 23 surface markers (Supplementary Table 1). We performed unsupervised clustering to define nine major clusters based on their expression patterns. F_SP HEM was more proficient at forming haematopoietic and endothelial lineages, including primitive (primitive erythrocyte progenitors (pEryP, CD41+/- CD71+c-kit-) and definitive blood subsets comprised of definitive haematopoietic progenitors (dHPC, CD41+/−c-kit+CD71+CD43+), pre-haematopoietic stem and progenitor cells (pre-HSPC, CD41+VE-cad+Tie2+CD34+c-kit+) and macrophage progenitors (MacP, CD41+/−c-kit+/−CD11b/c+CD44+CD45+Cx3Cr1+) and endothelial type 1 (Tie2⁺FLK1⁺CD31⁺VE-cad⁺c-kit⁻) and type 2 (Tie2⁺FLK1⁺CD31⁺VE-cad⁺c-kit+) clusters, whereas DP priMes had a higher potential to form VSM-enriched cells (CD41−VE-cad−CD71−Epcam−) (Fig. 1f, g and Supplementary Fig. 2). To further assess the functional differences of DP priMes and F_SP HEM, we established a long-term haematopoietic assay to promote the maturation of haematopoietic progenitors. We compared (i) the blood-forming potential and (ii) the ability to generate mature blood lineages of these two mesodermal populations. Defined numbers of FACS-sorted DP priMes and F_SP HEM (18k, 10k, or 5k) isolated from EB cultures were plated and analysed after 9.5 days. Whereas F_SP HEM generated haematopoietic cells under all tested conditions, DP priMes showed reduced blood-forming potential, particularly at lower cell inputs: For 5k cells, 100% of F_SP HEM formed colonies, but only 8% of DP priMes (Supplementary Fig. 3a, b). Additionally, for blood lineages analysis, we scored 8 randomly selected wells for each condition and analysed the number of live cells and the frequency of leukocytes (CD45+) and erythroid (CD71+) cells, the two main blood lineages forming in the assay (Supplementary Fig. 3c–f). F_SP HEM consistently showed higher number of live cells and increased frequencies of CD45+ and CD71+ cells in all analysed conditions compared to DP priMes (Supplementary Fig. 3c-f). Collectively, these results demonstrate that mesodermal populations, defined by FLK1/PDGFRα expression, differ significantly in their blood-forming capacity. F_SP HEM is functionally distinct from DP priMes with superior potential to form mature haematopoietic cells due to increased potential to form haematopoietic progenitors. This highlights the importance of FLK1/PDGFRα expression in marking functionally distinct mesodermal subpopulations during early haematopoietic development.

Lastly, to further underline the relevance of our ESC model, we performed a scRNA-seq characterisation of both stages of our model employing SORT-seq. By relying on the lineage defining markers FLK1, CD41, VE-cad, and c-kit, we isolated single cells corresponding to 7 distinct populations from the EB and HB stage (Supplementary Fig. 4a). Our analysis confirmed the generation of distinct lineages characterised by appropriate signature gene expression patterns, including pHPCs (primarily primitive erythrocyte progenitors), dHPCs, VSM-enriched cells, and endothelial lineage (Supplementary Fig. 4b–d). Additionally, we integrated the transcriptional profiles of our in vitro-generated cells with a mouse gastrulation atlas27, which includes cells isolated from E6.5–E8.5 embryos. A high proportion (39.8%) of the in vitro-generated cells could be directly projected onto the in vivo haemato-endothelial trajectory graph, evidencing the similarities between in vitro- and in vivo-generated cells (Fig. 1h). Taken together, our functional analysis, single-cell and bulk RNA-seq profiles confirm FLK1 and PDGFRα as appropriate markers to distinguish mesodermal populations and attest that the model faithfully recapitulates early mesodermal commitment and subsequent blood differentiation.

CRISPR screens identify the core transcription factors of HEM commitment
Having confirmed the developmental route giving rise to F_SP HEM, we performed loss-of-function CRISPR-Cas9 screening to identify factors regulating the transition from DP priMes towards F_SP HEM. We first established the CRISPR screen workflow using a library against transcription factors and chromatin regulators, which targeted 4077 genes using sets of five individual guide RNAs (EpiTF library) (Fig. 2a). We transduced the sgRNA library containing a GFP reporter into an ESC line constitutively expressing Cas928 to ensure optimal KO efficiency. Two days after transduction, sgRNA-expressing (GFP+) ESCs were sorted and used to initiate EB differentiation. We further confirmed the single guide representation at the start of the culture (Supplementary Fig. 5a). On day 5, we collected the cells and isolated the four mesodermal populations using FLK1 and PDGFRα expression (Fig. 2a). We then recovered and sequenced sgRNAs from the mesodermal populations and ranked them using MAGeCK. To discover genes that regulate the transition from DP priMes towards F_SP HEM, we compared sgRNA presentation in these two mesodermal populations. We generated an integrated ranked candidate gene list by calculating the rank mean of the three individual screens conducted in this way (Fig. 2b, c). By pinpointing “hits” where the targeting sgRNA abundance was either depleted or enriched in the F_SP HEM versus DP priMes population with high statistical ranking, we identified genes that likely act as potential drivers or repressors for this lineage transition, respectively (Fig. 2b, c). We additionally scored the top 200 depleted genes of all mesodermal transitions and identified common overlaps in the three independent CRISPR screens (Supplementary Data 1). Focusing on the transition of DP priMes towards F_SP HEM we identified 10 common overlaps for the top 200 depleted genes (Fig. 2d): Smad1, Etv2, Ep300, Rps27a, Mis18a, Ldb1, Rpa1, Med18, Six4, Hira. For the top 200 enriched genes, we identified four overlaps (Fig. 2e): Cop1, Zbtb7b, Zic3, and Npm1. Interestingly, all overlapping factors displayed a high screen performance and were among the top 15 depleted/enriched genes, respectively (Fig. 2c). As we also detected sgRNA in the populations T0 (ESCs collected at the beginning of the EB culture), DN, and P_SP, we could ask whether the identified “hit genes” had differences in guide abundance (cpm reads) specifically in the transition from DP towards F_SP mesoderm (Fig. 2f and Supplementary Fig. 5b). This was the case for the 14 overlaps that we detected in the individual screens. Importantly, ETV2, the best-known TF regulating DP priMes towards F_SP HEM transition, was among the top hits of the depleted genes (rank 2) serving as a positive control for our screening set-up.

Validation of Hit Genes that promote or repress haemato-endothelial mesoderm commitment
To prioritise hit genes for validation, we applied three selection criteria: genes had to be in the top 15 factors of most depleted or most enriched; genes had to be common to the top 200 hits of the three independent screens; and genes had to have a p value < 0.001. This resulted in a potential candidate list of 14 genes (10 depleted genes and four enriched genes) (Supplementary Fig. 6a). To assess whether those genes regulated F_SP HEM commitment, we cloned independent KO ESC lines and performed EB differentiation experiments to score their ability to form F_SP HEM. Importantly, we generated at least three independent KO lines for each candidate to ensure that their phenotype was indeed driven by the gene KO and compared them to controls (Ctrls) transfected with non-targeting guides. For three genes, Mis18a (kinetochore protein), Rps27a (ribosomal protein), and Rpa1 (cell cycle protein), we were not able to generate viable KO ESC lines, pointing to an essential role of those genes, and we had to exclude them from further analysis. Of the seven depleted candidates, four resulted in a significant decrease in the frequency of F_SP HEM: Smad1 (rank 1), Etv2 (rank 2), Ldb1 (rank 6), and Six4 (rank 9) (Fig. 3a, b). The KO lines exhibited an accumulation of cells at earlier developmental transitions: Etv2 KO, Smad1 KO, and Ldb1 KO had a significant increased frequency of DP priMes and Six4 KO of DN cells (Supplementary Fig. 6b–d).

To understand those genes’ impact on cellular differentiation, we further analysed the frequency of total FLK1+ cells (including both DP priMes and F_SP HEM) and calculated the ratio of F_SP HEM/total FLK+ cells in cultures derived from the KOs (Fig. 3c and Supplementary Fig. 6e). Importantly, cultures generated from KOs of our four validated candidates generated similar frequencies of total FLK1+ cells compared to the control that resulted in a significant decrease in the ratio of F_SP HEM/total FLK+ cells (Fig. 3b, c). This points to a specific role of these genes in regulating the transition of DP priMes towards F_SP HEM and not to a general defect of FLK1+ mesodermal commitment. In addition, no significant differences in the total number of live cells were detected between KO cultures or compared to controls, ruling out an effect on cell proliferation/viability (Fig. 3d). Surprisingly, the gene KOs for Ldb1, Smad1, and Six4 had an equally strong effect on arresting DP to further differentiate towards F_SP, as the known TF ETV2. We subsequently term these validated TFs F_SP HEM-driving TFs. Additionally, to understand if the identified TF factors lead to a delayed maturation of F_SP HEM differentiation, we extended the EB culturing time (Supplementary Fig. 6f–h). We could not detect any increase of F_SP HEM on EB day 6 in the KO cultures compared to day 5 and conclude that the identified F_SP HEM-driving TFs lead to disruption or block in differentiation, not a shift in timing (Supplementary Fig. 6f–h).

We next considered the enriched genes, analysing four hits and validating one: Zbtb7b (rank 2). EB cultures of Zbtb7b KO cells generated F_SP HEM at a significantly higher frequency than controls, leading to an increased ratio of F_SP HEM/ total FLK+ cells (Fig. 3e–g). Zbtb7b KO cells showed decreased frequencies of earlier transitions, including DP priMes and P_SP (Supplementary Fig. 6i–k). Importantly, Zbtb7b KO cultures contained a comparable number of live counts and total FLK1+ cells as did controls (Fig. 3h and Supplementary Fig. 6l). These results suggest that Zbtb7b KO cells have an enhanced potential to commit to F_SP HEM at the expense of DP priMes/P_SP, without affecting cell proliferation: Zbtb7b therefore functions as a repressor of F_SP HEM differentiation. Uniquely, EB cultures of Npm1 KO cells resulted in significantly fewer live cells, but F_SP HEM and DP priMes still formed, pointing to a general function of this gene in cell proliferation (Fig. 3e, h and Supplementary Fig. 6i). Due to the low number of surviving cells, significantly fewer F_SP HEM and DP priMes were generated, but a similar ratio of F_SP HEM/total FLK1+ cells were achieved compared to the Ctrl (Fig. 3e, g, h). These results agree with previous reports, which showed that Npm1 suppression inhibits ESC proliferation but does not have a general effect on cell differentiation of neural stem cells29,30. As we aimed to identify factors that act specifically on the differentiation of F_SP HEM, we excluded NPM1 from further analysis.

Taking advantage of our scRNA-seq dataset, we then analysed the gene expression of the validated candidates along the mesodermal differentiation trajectory. In agreement with previous reports31, Etv2 showed a peak of expression at the transition from DP towards F_SP mesoderm; Smad1 gene expression peaked earlier than Etv2, at the beginning of DP mesoderm development but was broadly expressed in all populations; while Ldb1, Six4 and Zbtb7b were equally expressed in the different mesodermal populations, with Ldb1 showing the highest and Zbtb7b the lowest level of expression (Fig. 3i). These data show that our candidate genes (except for Etv2), would have been difficult to identify by employing a differential gene expression analysis between DP priMes and F_SP HEM.

In summary, by applying strict criteria to selecting genes, we were able to validate our hits with high efficiency. We confirmed the critical role of the known master regulator ETV2 in enabling F_SP HEM differentiation and identified additional factors, including SMAD1, LDB1, SIX4, and ZBTB7b, which act as drivers and repressors of F_SP HEM commitment.

Haematopoietic and endothelial lineage commitment is differentially regulated by core TFs
We showed that the absence of the validated F_SP HEM-driving TFs led to a marked reduction of F_SP HEM cells, whereas the initial steps of primitive mesodermal differentiation, indicated by FLK1 upregulation, were not affected. On the other hand, loss of Zbtb7b led to an increased frequency of F_SP HEM cells among the FLK1+ cells compared to Ctrls. To further understand the downstream effect of our validated candidates on the developmental potential of the formed FLK1+ mesoderm, we proceeded to drive the differentiation of the corresponding KO lines towards haematopoietic and endothelial lineages. To achieve this, we sorted total FLK1+ cells from KO EB cultures, which contained both F_SP and DP mesodermal cells, and analysed their fate specification with our established high-dimensional flow cytometry panel. We compared these cultures to Ctrls transduced with non-targeting guides. As expected, and in line with the literature15,32, Etv2 KO cells had a complete block of haematopoietic and endothelial lineage commitment; only VSM-enriched and uncommitted EpiSC-like cells (CD41-VE-cad-CD71-Epcam+) formed in Etv2 KO cultures (Fig. 4a, b). Ldb1 KO cells, similarly to Etv2 KO cells, did not differentiate to haematopoietic and endothelial cells, while contrastingly Six4 KO cells were still able to give rise to haematopoietic lineages, including both primitive and definitive subsets, with only a minor decrease (Fig. 4a, b). Additionally, Six4 KO cells showed a marked differentiation block towards the endothelial lineage, especially endothelial cell type 1 cluster (Fig. 4a, b). Among all analysed validated candidate genes, fewest live cells were obtained from Smad1 KO cultures (Supplementary Fig. 7a). Still, Smad1 KO cells were able to form both haematopoietic and endothelial lineages, albeit at a highly reduced frequency and number (Fig. 4a, b). For endothelial lineages, similar to Six4 KOs, Smad1 KO cells showed mainly a reduction of the endothelial cell type 1 cluster (Fig. 4a, b). Zbtb7b KO cells resulted in the highest yield of live cells after differentiation and formed all lineages with an increase in haematopoietic lineages and endothelial cell type 2 cluster (Fig. 4a, b and Supplementary Fig. 7a). This result indicates that Zbtb7b KO mesoderm is already committed towards haematopoietic and endothelial fate and their enhanced capacity during HB culture could be a direct effect of the increased frequency of F_SP HEM cells in the starting culture. In summary, our data reveal that each identified TF has a distinct role during haematopoietic and endothelial lineage differentiation, and that loss of function of those TFs leads to the formation of mesodermal subsets with a defined lineage differentiation bias.

Heterogeneity of mesodermal cells is regulated by core TFs
Our previous differentiation experiments revealed that the developing mesoderm has distinct lineage differentiation biases depending upon individual core TF KOs. This points towards the presence of mesodermal subpopulations and cellular heterogeneity during mesoderm development. To assess this possibility and to obtain greater insight into the gene expression perturbations in the respective KO lines, we performed probe-based 10x Chromium scRNA-seq characterization of total FLK1+ cells in Ctrl and KO samples. Following quality control of the obtained data, we retrieved 69,174 cells (Ctrl: 17,725 cells, Etv2 KO: 7172 cells, Ldb1 KO: 8279 cells, Six4 KO: 9127 cells, Smad1 KO: 8018 cells, and Zbtb7b KO: 9673 cells) with a median feature count of 6054 and a median sequencing depth of 21,258 counts per cell. After optimizing the resolution parameter for Louvain clustering analysis and conducting unsupervised dimensionality reduction and uniform manifold approximation and projection (UMAP) analysis in Seurat, we identified 10 different clusters (C) and assigned them to developmental stages (Fig. 5a).

The clusters did not form distinct, well-segregated populations, instead, distinct subpopulations are positioned next to each other, underpinning continuous developmental processes. Based on a differential gene expression analysis incorporating well-established mesodermal and haemato-endothelial genes, the developmental trajectory towards HEM could be defined (Fig. 5a, b and Supplementary Table 2) from C1-C2-C3-C4-C5-C6 (priMes 1/2; Foxf1hi mesoderm; pre-HEM and HEM 1/2, respectively). We denoted C1 (priMes 1) as the most naïve mesodermal cluster due to high expression of early mesodermal markers such as Eomes, Mesp1 and Mixl1 (Fig. 5b). By contrast, the most advanced cluster along the HEM trajectory C6 (HEM 2) was characterised by upregulation of known haematopoietic genes crucial in the process of endothelial-to-haematopoietic transition and blood development including Tal1, Gata2 and Runx1 (Fig. 5b, c). Following our previous nomenclature of DP priMes and F_SP HEM, we assigned the clusters C1-4 to DP priMes and C5 and C6 to F_SP HEM, based on the expression of FLK1 (encoded by KDR gene) and PDGFRa (Fig. 5b, c). Combining our two scRNA-seq datasets further supports this assignment (Supplementary Fig. 7b). Strikingly, several clusters were absent in, or specific to, individual KOs and cluster frequencies are distinct across conditions: for example, Etv2 KO and Ldb1 KO cells are stalled early during development, with only C1-C3 and C1-C5 being present, respectively (Fig. 5a, d, e). This highlights the early and critical role of ETV2 during HEM development and shows that LDB1 plays a subsequent role, allowing the cells to progress further. Interestingly, Etv2 starts to be upregulated in C4 (pre-HEM) and has its highest expression in C5 (HEM 1) (Fig. 5b, c). The early developmental block of Etv2 and Ldb1 KO cells agrees with the differentiation experiments where neither endothelial nor haematopoietic lineages develop in those KOs (Fig. 4a). In Six4 KO cell cultures, all clusters were present that were in the Ctrl samples, albeit with altered frequencies, but—uniquely among the F_SP-driving TFs—Six4 KOs were able to form cells belonging to the most advanced C6, albeit at highly reduced frequency (Fig. 5a, d, e). This is in agreement with our differentiation experiment, where Six4 KO cells were able to form all haematopoietic lineages (Fig. 4a). Concurrently, the early C1 (primitive mesoderm 1) was expanded in Six4 KO cell cultures compared to the Ctrl (Fig. 5a, d, e). Smad1 KO cells were the most different to the Ctrl and the other candidate KOs, forming three clusters that were almost absent in the other cultures: C7 (neuroectoderm), C8 (mixed mesoderm) C9 (early endothelial progenitors). C7 was characterised by high expression of Nnat, Dll1 and Dll3, which are collectively expressed during neural cell development (Fig. 5b and Supplementary Table 2). C8 (mixed mesoderm) did not resemble any mesodermal population commonly found during development and was characterised by high expression of Tll1 (Tolloid-Like Protein 1) and Opticin (Optc gene) (Fig. 5b and Supplementary Table 2). Alongside, C9 (early endothelial progenitors) was also mainly found in Smad1 KO cell cultures and was defined by high expression of endothelial markers including Plxnd1, Rasip1, and Rhoj (Fig. 5a, b and Supplementary Table 2). Despite the different composition of mesodermal subpopulations, Smad1 KO cells were still able to form blood and endothelial lineages albeit at highly reduced frequency (Fig. 4a). Zbtb7b KO cells gave rise to a higher frequency of C5 (HEM 1) and a lower number of cells in C3 (Foxf1hi mesoderm) compared to the Ctrl (Fig. 5a–e). C5 was characterised by the highest expression of Etv2 and additional TFs associated with haematopoietic development, including Gata3 and Lmo2 (Fig. 5b, c). Therefore, C5 is clearly committed towards blood development (Fig. 5b, c). C3 was characterized by expression of early mesodermal markers like Hand1 and Mest and a high expression of the TF Foxf1 (Fig. 5b, c and Supplementary Table 2). To note, a previous study implicated the expression of Foxf1 in inhibiting haematopoietic lineage commitment during mesoderm specification33. A reduced frequency in C3 or Foxf1 expressing mesodermal cells could therefore result in the observed phenotype of enhanced F_SP HEM commitment of Zbtb7b KO cells and increased commitment towards the haematopoietic and endothelial lineage (Fig. 4a). The intermediate C4 (pre-HEM) revealed only two distinct marker genes (Lmo2 and Fgf3) and was present at low frequencies in all analysed conditions (ranging from 2 to 6% of the cells in the different genotypes) (Supplementary Table 2). Zbtb7b KO cells had the highest frequency of C4 among all analysed conditions (6.53%), pointing to an advanced commitment towards the HEM lineage (Supplementary Fig. 7c). Cells belonging to C0 did not express FLK1 but rather the pluripotency marker Nanog; accordingly, we denoted them as uncommitted cells which were equally present in all genotypes (between 1-3%) (Fig. 5a, b, d). Cells belonging to C0 were most likely caught during FACS sorting due to a less stringent gating strategy and were excluded from further analysis. To characterise all cells committed towards mesodermal fate we did not select for cells that only highly expressed FLK1, accepting the risk of sorting and sequencing FLK1 neg cells.

To further analyse the developmental order of the clusters and to not solely rely on the expression analysis of known marker genes, we performed condition-wise pseudotime analysis (Fig. 5f and Supplementary Fig. 7d). We used C1 (priMes 1) as the root, based on its naïve mesodermal transcriptomic profile. We confirmed C6 (HEM 2) as the most committed mesodermal population following the haemato-endothelial mesodermal development trajectory. Etv2 KO cells, Ldb1 KO cells and Six4 KO cells followed the same trajectory as the Ctrl cells, with the former two being stalled early during development. Intriguingly, Zbtb7b KO cells, which are largely composed of the same clusters as the Ctrl, formed the most advanced C6 (HEM 2) via a different trajectory, mainly through C5 (HEM 1), and included very few cells belonging to C3 (Foxf1hi mesoderm) (Fig. 5f). This could indicate that Zbtb7b KO cells do not form C3 or pass through it faster than the Ctrl cells. Smad1 KO cells appeared to undergo an entirely different developmental trajectory compared to the Ctrl and formed a trajectory of the Smad1 KO specific clusters via C1-C7-C8-C9 (Fig. 5f). We additionally, projected the in vitro generated cells of the individual conditions onto the in vivo haemato-endothelial trajectory graph (Fig. 1e and Supplementary Fig. 7e). In agreement with our UMAP analysis, Etv2, Ldb1 and Smad1 KOs cells display an early developmental block, whereas Six4 KO cells can be projected further onto the trajectory (Supplementary Fig. 7e). Smad1 KO cells had the lowest frequency of all analysed conditions (16.5% vs. 20.4% for Ctrl) which agrees with the alternative trajectory that Smad1 KO cells follow (Fig. 5f and Supplementary Fig. 7e). Collectively, our scRNA-seq data of the candidate gene KOs reveals that each TF impacts distinctly on the mesodermal developmental trajectory and distinct mesodermal subpopulations form during EB cultures, which are differently impacted by candidate gene KO.

Methods
Cell culture and cell line generation
Mouse ESCs (HA36CB1, 129×C57BL/6) were cultivated on 0.2%-gelatine-coated dishes in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 15% foetal calf serum (FCS), 1x non-essential amino acids, 1mM L-Glutamine, LIF, and 0.001% β-mercaptoethanol.

The KO cell lines were generated by co-transfecting pX330-U6-Chimeric_BB-CBh-hSpCas9 (Addgene 42230) with two sgRNAs (sequences see Supplementary Table 3). Control samples were transfected with non-targeting sgRNAs (sgRNA1_GCGTATCTACCCTACCGCCG, sgRNA2_GCGGTTACCGCGAAAACCAT). pRR-Puro recombination reporter52 (Addgene 65853) was co-transfected, and after 36h, cells were treated with 2μg/ml of puromycin for another 36h. KO clones were validated by Sanger sequencing. KO validation by western blot detection was performed as described previously53 for the identified candidates Etv2, Ldb1, Six4, Smad1 and Zbtb7b (Supplementary Fig. 8a–e). Transfections were conducted using Lipofectamine 3000 reagent (Thermo Fisher Scientific) at a 2:1 Lipofectamine/DNA ratio in OptiMEM (Thermo Fisher Scientific).

Embryoid body (EB) culture for mesoderm differentiation
Twenty-four hours prior to the initiation of EB cultures, ESCs were transferred into gelatinized plates containing Iscove’s Modified Eagle Medium (IMDM) (Gibco). For the generation of EB cultures, ESCs were trypsinized then plated at 10,000 cells/ml in non-adherent 10cm2 petri dishes in EB medium (IMDM supplemented with 1% L-Glutamine (Gibco), 10% FCS (Gibco), 0.6% transferrin (Roche, 10652), 50µg/ml ascorbic acid (Sigma, A4544) and 0.03% monothioglycerol (Sigma, M6145). BMP4 was added from day 0 of EB culture, and bFGF, activin A and VEGF from day 2.5 (all Peprotech) all at 5ng/ml. After 5 days in culture, EBs were harvested, and TrypLE Express Enzyme (1×) (Gibco, 12605036) was used to generate a single-cell suspension.

Haemangioblast (HB) culture for haematopoietic and endothelial commitment
Prior to HB culture, mesodermal populations underwent FACS sorting (for either DN, P_SP, DP priMes, F_SP HEM or total FLK1+) and 0.5×106 cells were cultured on 0.2%-gelatine-coated plates in IMDM supplemented with 1% penicillin–streptomycin, 1% L-Glutamine, 15% FCS (Gibco), 0.06% transferrin (Roche), 0.03% monothioglycerol (Sigma, M6145), 50mg/μL of ascorbic acid (Sigma, A4544), 5ng/ml VEGF (Peprotech) and 10ng/mL IL-6 (Peprotech). After 2.5 days in culture, cells were harvested for flow cytometry analysis. TrypLE Express Enzyme (1×) (Gibco, 12605036) was used to generate a single-cell suspension.

Long-term haematopoietic assay in liquid media
Defined numbers (18,000, 10,000, or 5000 cells) of FACS-sorted F_SP HEM or DP priMes were plated on 0.2%-gelatine-coated 96-well plates. For each cell number a minimum of 24 wells were seeded. The cells were first cultured for 2.5 days in HB culture media (to induce the differentiation to haematopoietic progenitors) and subsequently in conditions supporting the maturation of haematopoietic cells in IMDM supplemented with 1% L-Glutamine, 10% FCS (Gibco), 0.6% transferrin (Roche), 0.03% monothioglycerol (Sigma, M6145), 50mg/μL of ascorbic acid (Sigma, A4544), 100ng/ml SCF (Peprotech), 1ng/ml IL-3 (Peprotech), 10μg/ml G-CSF (Peprotech), 5μg/ml IL-11 (Peprotech), 5μg/ml IL-6 (Peprotech), 5μg/ml TPO (Peprotech), 10μg/ml M-CSF (Peprotech) and 2000U/ml erythropoietin (R&D Systems). After 9.5 days in culture wells were scored for the presence of haematopoietic cells and cells were harvested for flow cytometry analysis. TrypLE Express Enzyme (1×) (Gibco, 12605036) was used to generate a single-cell suspension.

Flow cytometry
EB cultures. Single-cell suspensions were obtained on day 5 of differentiation. For cell-surface labelling, cells were incubated for 30 min at 4°C with a 1:200 solution of anti-CD309 (FLK1) (APC, BD Bioscience, clone Avas12a1) and anti-CD140a (PDGFRα) (PE, BD Bioscience, clone APA5) monoclonal antibodies. LIVE/DEAD Fixable Near-IR Dead Cell Stain (L34975, Invitrogen) was used to determine cell viability. Sample data were acquired using a BD Symphony (BDBiosciences) flow cytometer, and data were analysed using FlowJo software (version 10.7, TreeStar), then visualized with Prism (version 5.0a).

Haemangioblast (HB) cultures. Single-cell suspensions were obtained at day 2.5 of differentiation.
Conventional FACS analysis: Antibody staining was performed for 30 min at 4°C with a 1:200 solution of anti-CD41 (BV650, BD Biosciences, Clone MWReg30) anti-VE-cadherin (eFluor 660, BD Biosciences, clone eBioBV13), and anti-cKit (PE-Cyanine 7, BDBiosciences, clone ACK2) monoclonal antibodies using a BD Symphony (BDBiosciences) flow cytometer. LIVE/DEAD Fixable Near-IR Dead Cell Stain (L34975, Invitrogen) was used to determine cell viability.
Spectral flow cytometry: Cells were first labelled with Zombie NIR fixable viability dye (BioLegend, 1:500) for the exclusion of dead cells. Before antibody staining, cells were incubated with TrueStain FcX™ and TrueStain Monocyte Blocker (BioLegend) to reduce nonspecific binding. Surface antigens were labelled using an antibody panel consisting of the following 23-markers (Supplementary Table 1): From BD biosciences: anti-CD117(cKit) (BUV395, Clone 2B8), anti-CD43 (BUV496, Clone 1B11), anti-CD45 (BUV563, Clone 30-F11), anti-CD9 (BUV661, Clone KMC8), anti-CD44 (BUV737, Clone IM7), anti-CD31 (BUV805, Clone 390), anti-Epcam (CD326) (BV480, Clone G8.8), anti-CD93 (BV605, Clone AA4.1), anti-CD41 (BV650, Clone MWReg30), anti-CD14 (FITC, Clone rmC5-3), anti-CD71 (RB780, Clone C2), and anti-CD47 (PE-CF594, Clone miap301). From BioLegend: anti-CD64 (BV421, Clone X54-5/7.1), anti-CD16/32 (BV711, Clone 93), anti-CX3CR1 (BV785, Clone SA011F11), anti-CD202b (Tie2) (PE, Clone TEK4), anti-CD34 (PE-Cy5, Clone MEC14.7), anti-CD309 (FLK1) (PE-Cy7, Clone Avas12), anti-Ly6C (AlexaFluor 700, Clone HK1.4), anti-Sca-1 (APC-Fire 750, Clone D7), and anti-CD11b (APC-Fire 810, Clone M1/70). From Thermo Fisher: anti-CD11c (PE-Cy5.5, Clone N418), and anti-VE-cadherin (CD144) (eFluor 660, Clone BV13). Surface staining took place on ice for 20 min in phosphate-buffered saline (PBS), followed by three PBS washes. Cells were subsequently acquired using a Cytek Aurora 5L spectral flow cytometer (Cytek Biosciences).
Data preprocessing and export: Doublets were excluded based on side scatter area versus height, and non-viable cells identified by Zombie NIR™ positivity were removed using FlowJo (v10, TreeStar). The resulting compensated, pre-gated dataset was exported in FCS format for further analysis with R software (version 4.1.2).
Transformation and normalization of data: The flowCore R package was utilized to import the FCS files via the read.flowSet() function. Fluorescence intensity values were transformed using an arcsinh function to address the wide dynamic range. To facilitate direct comparisons across samples, data underwent percentile normalization, scaling expression values to a consistent 0–1 range.
Dimensional reduction and clustering: All 23 markers, including lineage- and progenitor-associated proteins, were subjected to dimensional reduction through uniform manifold approximation and projection (UMAP), implemented via the umap R package. For unbiased identification of cellular subsets, FlowSOM clustering was conducted using the same transformed and normalized data. FlowSOM-derived clusters were grouped through metaclustering, with subsequent refinement by manual annotation guided by expert assessment. Cluster identities were determined based on median marker expression levels, merging clusters exhibiting similar marker profiles. This approach defined clear subsets of primitive and definitive haematopoietic progenitor cells, endothelia, and VSM-like cells. A dot plot depicting median marker expression per cluster is shown in Fig. 1g to illustrate cluster characterization.

Long-term haematopoietic assay in liquid media. Single-cell suspensions were obtained on day 9.5 of differentiation. For cell-surface labelling, cells were incubated for 30 min at 4°C with a 1:200 solution of anti-CD45 (PerCP-Cyanine 5.5, BD Biosciences, Clone 3D-F11) and anti-CD71 (RB780, BD Biosciences Clone C2) monoclonal antibodies. LIVE/DEAD Fixable Near-IR Dead Cell Stain (L34975, Invitrogen) was used to determine cell viability. Sample data were acquired using a BD Symphony (BDBiosciences) flow cytometer, and data were analysed using FlowJo software (version 10.7, TreeStar), then visualized with Prism (version 5.0a).

Poly-A RNA-sequencing of EB cultures on day 5. Viable 0.5×106 cells of DN, P_SP, DP priMEs, and F_SP HEM were FACS sorted on day 5 of EB differentiation, using FLK and PDGFRa surface expression. Subsequently, RNA was isolated using Qiagen RNAeasy kit (Qiagen cat. 74134). The quality of the isolated RNA was determined with a Fragment Analyzer (Agilent, Santa Clara, California, USA). The TruSeq Stranded mRNA protocol (Illumina, Inc., California, USA) was used in the succeeding steps. Briefly, total RNA samples (100–1000 ng) were poly A enriched and then reverse-transcribed into double-stranded cDNA. The cDNA samples were fragmented, end-repaired, and adenylated before ligation of TruSeq adapters containing unique dual indices (UDI) for multiplexing. Fragments containing TruSeq adapters on both ends were selectively enriched with PCR. The quality and quantity of the enriched libraries were validated using the Fragment Analyzer. The libraries were normalized to 10nM in Tris-Cl 10mM, pH 8.5 with 0.1% Tween 20. Single-end 100bp were sequenced on Novaseq 6000 (Illumina, Inc., California, USA), and an average of ~20 million reads per sample were obtained.
Data Analysis: Reads were mapped to the GRCm39.107 genomic reference and count tables generated using STAR-2.7.754. Count tables were filtered and only genes with a minimum of 10 reads and occurrences in at least three conditions were retained (24155 genes). PCA was conducted using DESeq2 v1.38.3 R package55.

Single-cell sequencing. SORT-seq of EB cultures day 5 and HB cultures day 2.5
Viable single cells underwent single-cell FACS-sorting into 384-well cell capture plates (Single Cell Discoveries, Netherlands) using Flk1, CD41, c-kit, and VE-cad surface expression (gating strategy in Supplementary Fig. 4a). Each well contained 50 nl of barcoded primers and 10 µl of mineral oil (Sigma M8410). After sorting, plates containing cells were centrifuged at 1500×g for 1 min then placed on dry ice before storage at −80°C. Single-cell RNA sequencing was performed by Single Cell Discoveries according to an adapted version of the SORT-seq protocol56, with primers as described in ref. 57. Briefly, cells were heat-lysed at 65°C followed by cDNA synthesis, before the barcoded material from each plate was pooled into its own library and amplified by in vitro transcription (IVT). Library preparation was then carried out following the CEL-Seq2 protocol58 to generate a cDNA library for sequencing using TruSeq small RNA primers (Illumina). The DNA library was paired-end sequenced on an Illumina Nextseq 500, high output, with a 1×75bp Illumina kit (read 1: 26 cycles, index read: 6 cycles, read 2: 60 cycles).
Data analysis: During sequencing, read 1 was assigned 26 base pairs and was used to identify the Illumina library barcode, cell barcode, and unique molecular identifier (UMI). Read 2 was assigned 60 base pairs and used to map to the reference genome Mus musculus (GRCm38) version 99 with STARSolo 2.7.3a. Briefly, mapping and generation of count tables were automated using the STARSolo 2.7.3a aligner. Unsupervised clustering and differential gene expression analysis/positive marker discovery were performed in Seurat. Count tables were imported into R, where a single Cell Experiment object was constructed and subsequently filtered based on library size, feature count, spike-in count, and mitochondrial count using the isOutlier function from the scater v1.26.1 R package. Following quality control, the dataset was analysed using the Seurat v4.3 R package, using the SCTransform workflow59. This included normalization, scaling, and linear dimensionality reduction. Integration of selected haemato-endothelial cells from the publicly available mouse gastrulation atlas27 with our in vitro scRNA-seq dataset was performed using Seurat. To complement this integration, the force-directed graph of the haemato-endothelial landscape was reconstructed by incorporating supplementary data from the atlas publication. Within the integrated dataset, in vitro population labels were assigned to the in vivo cells from the atlas based on the k=5 nearest neighbours. For RNA velocity analysis, the Seurat data were exported, and an annotationData object was manually constructed in Python. Subsequently, scvelo v0.2.5 (ref. 60) was applied to it for comprehensive RNA velocity analysis. The latent time of each cell from the dynamical model was used to calculate an average per cluster, thereby arranging the clusters along the developmental trajectory.

10x chromium fixed RNA profiling. Isolated cells were fixed using the 10x Chromium Next GEM Single Cell Fixed RNA Sample Preparation Kit (PN-1000414) and stored at −80°C. The quantity of fixed cell suspension was assessed using an automated cell counter (LUNA-FX7, Logos). Library preparation was conducted using the 10x Chromium Fixed RNA Profiling Reagent Kits for Multiplexed Samples. Briefly, four fixed samples underwent probe hybridization, pooling, and loading onto the chip. Approximately 66,000 cells from the sample pools were loaded into the 10x Chromium X. Library preparation followed the manufacturer’s guidelines (Guide CG000527, Rev E). For sequencing, the resulting libraries were processed on two lanes of an Illumina NovaSeq X Plus 10 billion flow cell with a 150bp paired-end read configuration. An average sequencing depth of around 25,000 reads per cell was achieved.
Data analysis: Single-cell RNA-seq reads were processed with the 10x software Cell Ranger Multi version 7.2.061 with the Mus Musculus GENCODE GRCm39 annotation (Release M31-2023-01-30) to generate the single-cell gene expression matrix. Count tables were imported into R using the Read10X function of Seurat v4. A Seurat object was created and filtered based on library size, feature count, and mitochondrial count, using the isOutlier function of the scater v1.26.1 R package62. Normalisation, scaling, and variable feature detection were performed using the SCTransform workflow in Seurat. Subsequently, linear dimensionality scaling, unsupervised clustering and differential gene expression analysis/positive marker discovery were performed in Seurat. Integration with other scRNA-seq datasets was performed using Seurat functions SelectIntegrationFeatures, PrepSCTIntegration, FindIntegrationAnchors, and IntegrateData with SCT as normalisation method. For the Mouse Gastrulation Atlas in vivo dataset27, condition-wise integration was conducted on (per-condition) down-sampled subsets. Within the mixed dataset, labels have been propagated to the k=3 nearest neighbours. During integration with our previous in vitro EB dataset (sort-seq) a single per-cluster down-sampled dataset was used. For pseudo-time analysis, the Seurat object was exported to a CDS object using the SeuratWrappers package. Pseudotime trajectory analysis was conducted using Monocle 363.
The trajectory graph was constructed on the previously established UMAP embeddings from Seurat via the “learn_graph” function, using default options, but “use_partition” being set to false. Cluster 3 cells were designated as the root population based on their gene expression profile, indicating a more immature state. Cells were ordered along this trajectory using the “order_cells” function to establish pseudotime coordinates.

CRISPR screens in embryoid body EB cultures. Lentiviral plasmid library targeting TFs and chromatin-regulators (EpiTF library) was kindly provided by Michlits et al.64. Three independent CRISPR screens were performed with a guide representation of ~500x. For lentiviral transduction 60×106 ESCs that constitutively expressed Cas928 were spinfected at a multiplicity of infection (MOI) of 0.2 with polybrene, by centrifugation 500×g for one hour, at 37°C. After spinfection, cells were incubated at 37°C, 5% CO2 for 48h and subsequently underwent FACS based on GFP expression to isolate sgRNA+ cells. Per screen, 12×106 sorted sgRNA+ cells were used for EB differentiation. Mesodermal populations of interest underwent FACS on day 5 based on FLK1 and PDGFRα expression. A time point zero sample (T0) was collected at the start of the cultures.
CRISPR screen sequencing library preparation and sequencing: Genomic DNA was isolated with the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s instructions. DNA concentrations were measured using the Qubit dsDNA BR assay, and the entire samples were used for the first PCR amplification. In PCR1, sgRNAs were amplified and equipped with adapters using Herculase II Fusion DNA Polymerase (Agilent, 600677). Following, PCR products were pooled and cleaned up by Qiagen MinElute PCR Purification kit following manufacturer’s instructions (Qiagen) and subsequently size selected with 0.7× Ampure XP beads, according to the manufacturer’s instructions. DNA concentration of cleaned-up PCR products was calculated by Qubit dsDNA HS assay. In PCR2, NEB next dual indices (NEB) were added to the amplicons using NEB Next Ultra™ II Q5 Master Mix (NEB). Following, PCR products were pooled and cleaned up by Qiagen MinElute PCR Purification kit following manufacturer’s instruction (Qiagen) and subsequently size selected with 0.7× Ampure XP beads, according to the manufacturer’s instructions and analysed on an Agilent TapeStation 2000 using High Sensitivity D1000 screen tape. Exact library quantification was carried out via qPCR using the KAPA Library Quantification Kit (Roche). Finally, an equimolar pool of all samples was established and sequenced on a NovaSeq 6000 sequencing machine with 100bp single read configuration.
CRISPR screens data processing: Basecalling and demultiplexing were performed using bcl2fastq2 v2.20.0.422 (Illumina), and sgRNA-sequences were isolated from the reads using cutadapt v3.1 via hard-clipping. Next, sgRNA-sequences were aligned to the library using bowtie2 v2.3.5 and a count table was assembled. Finally, sgRNA abundances were ranked on the gene-level using the MAGeCK v0.5.9 test module65. Rankings were then integrated by calculating the mean rank, and p values were combined by Fisher’s method. Duplicate mean ranks were resolved by additional sub-ranking based on the best rankings across all screens.

Statistics and reproducibility
No statistical method was used to pre-determine sample size. All experiments were confirmed by replication at least once. For each generated KO line, three independent clones were generated. The exact handling of replicates is depicted in figure panels. Randomization/Blinding is not relevant for the experiments performed in this study. Samples were allocated to either wild type control or mutant and processed in parallel through identical analysis pipelines.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overview: This manuscript reports a novel transcription factor network that drives the transition of murine primitive mesoderm to hemato-endothelial mesoderm (HEM)—extending previous work that has identified Runx1, Gata2 (and more recently, ETV2) as key regulators of early blood lineage commitment. Using a CRISPR-Cas9 knockout screen that targets TFs in a mouse ESC blood differentiation model, the authors identify four novel transcriptional regulators (Lbd1, Smad1, Six4, and Zbtb7b) that control lineage specification from primitive toward hemato-endothelial mesoderm. Teske and colleagues subsequently perform loss-of-function studies by knocking out each TF individually, which perturbs normal hematopoiesis in vitro, thus validating these genes as functional regulators of hemato-endothelial lineage commitment. Taken together, this technically rigorous screen resulting in identification of a novel TF network crucial for HE commitment advances our understanding of the complex networked regulators of murine hematopoiesis, which will inform ongoing and future efforts to generate hematopoietic cells from pluripotent stem cells (in particular, because generation of specific blood lineages hinges upon modulation of distinctly patterned subsets of mesoderm at the earlies stages of embryonic development).\nSummary of study: Teske and colleagues first characterize and validate an in vitro EB-culture mESC blood differentiation model, based off prior protocols by Gordon Keller’s and Georges Lacaud’s groups. They describe the progressive formation of distinct mesodermal entities defined by FLK1 and PDGFR , where cells progress from populations that are (1) double-negative to (2) PDGFR  single-positive to (3) double-positive to (4) FLK1 single-positive, consistent with mesodermal commitment. RNA-seq reveals progressive downregulation of pluripotency markers and upregulation of mesodermal and hemato-endothelial genes along this developmental trajectory, while FLK1-single positive populations exhibit increased developmental potential for blood lineages (assayed for CD41 and VE-Cadherin).\nNext, the authors perform a pooled CRISPR KO screen to identify putative drivers and repressors of hemato-endothelial (HEM) commitment and recover a list of candidate regulators of the transition from primitive mesoderm (double positive) toward FLK1-single positive HEM, identifying top depleted and enriched genes. The authors subsequently generate independent KO ES cell lines and differentiate them to assess relative competence to generate Flk1+ single-positive mesoderm, validating Smad1, Ldb1, Six4 as drivers—and Zbtb7b as a repressor—of the HE mesodermal commitment from primitive mesoderm. ETV2 was recovered as a top depleted hit and confirmed as a driver of primitive-to-HE mesoderm transition in accordance with previous studies, serving as a positive control.\nTo further uncover the effects of each gene KO on the downstream differentiation potential of HE mesoderm, the authors drive mesoderm cells toward hematopoietic and endothelial lineages, followed by analysis of fate specification, revealing that loss of function of each TF significantly biases the lineage differentiation of mesoderm subsets. Finally, the authors perform scRNA-seq characterization of FLK1+ cells in each KO line and identify 10 clusters corresponding to different developmental timepoints, finding that several clusters are absent in or specific to individual TF KOs, and that frequencies are distinct across genetic perturbations. Through pseudotime analysis, they construct a mesodermal developmental trajectory and demonstrate that each regulator TF distinctly modifies the trajectory of lineage commitment.\nMajor points:\n1. The screen is performed on the basis of loss of PDGFR  expression in FLK1+ cells as demarcating the transition from primitive to hemato-endothelial mesoderm. While the authors do show in Figure 1c/d that Flk1+ single-positive cells are more competent to form VeCad+ CD41+ cells (and to give rise to hematopoietic and endothelial lineages), there are still significant numbers of cells that do not induce VeCad/CD41 expression at EB Day 5, suggesting that the Flk1 SP population is heterogenous. The overall significance of the screen could be strengthened by molecular characterization of more mature blood cells differentiated from the FLK1+PDGFR - (F_SP HEM) versus FLK1+PDGFR + (DP priMes) fractions respectively, beyond merely showing that the SP fraction generates slightly higher percentages of VeCad+ and CD41+ cells. While the bulk and scRNA-seq data on EB Day 5 cells is convincing in demonstrating that early mesodermal markers and hematopoietic and endothelial markers are both segregated and enriched in DP_SP and F_SP HEM cells respectively, further functional and molecular characterization of specific blood cell types descending from these fractions of mesoderm (beyond solely flow cytometry analysis of surface markers in Fig 1e at 2.5 days post-EB culture) would reinforce the biological significance of PDGFR  downregulation. (For example, demonstration of enhanced emergence of HSCs or of other cell types, from the VeCad+CD41+ fraction arising from F_SP as compared to DP preMes (or even from the bulk F_SP HEM population as compared to DP priMes) would provide further assurance that PDGFR  downregulation does serve as a developmentally relevant transition.)\n2. In this study, characterization of in vitro differentiated hemato-endothelial lineages (HB culture) is performed at 2.5 days past FACS isolation of Day 5 EBs. The developmental significance of the authors’ findings could be significantly strengthened if they examined functional effects of modulating any of the four TFs on blood cells further differentiated down their developmental trajectory, which would lend credence to biological and functional implications of perturbing early mesoderm patterning for hematopoiesis. (For example, some functional effects of Zbtb7b KO on EB-derived cells after more than 2.5 days of HB culture would be interesting, and it would be especially of relevance to understand if knock-out of Zbtb7b might skew differentiation or enhance the production of certain more mature blood lineages.) Moreover, because there remains incomplete understanding of how faithful the in vitro differentiation of murine ES cells mimics hematopoiesis in vivo, a limitation of the study is validation of the TFs in the context of murine hematopoiesis. While generating knockouts and evaluating hematopoiesis in murine embryos is beyond the scope of this study, some discussion of the limitations of the study for revealing in vivo mechanisms of hematopoiesis is warranted.\n3. While not necessarily a pivotal issue with regard to the validity of the screen, it would strengthen the claim that each TF is specifically a negative or positive regulator of mesodermal commitment if authors could demonstrate that at least one of the KO phenotypes could be rescued by reintroducing the missing TFs exogenously through a lentiviral vector, etc.\nMinor points:\n1. While the authors quantified sgRNAs in the T0 ESCs and were thus able to identify differences in guide abundance between DP and FLK1+ single-positive mesoderm, it would be informative and strengthen the robustness of this specific screening platform to show that sgRNA representation in the transduced mESCs (post-selection for Cas9-GFP+ cells) mirrors the representation of the EpiTF plasmid library, which would demonstrate that sgRNAs were introduced into ESCs without significant biases (e.g., a simple Lorenz curve in the supplement to show sgRNAs are uniformly expressed in ESCs without biases).\n2. Typo on page 9, line 234: “but was broadly expressed in all populations; while Ldb1, Six4.”\n3. Typo on page 11, line 346: “sort due to a less stringent gating strategy.”\n4. Page 11, line 242 “Supp Fig 5b” should instead be “Supp Figure 4b.”", "reviewer_2": "In this manuscript, Teske et al. describe the identification of novel transcription factors involved in specifying hematovascular mesoderm (HVM). Using CRISPR/Cas9 screening, authors revealed that Smad1, Ldb1, Six4, and Zbtb7b had a major impact on the formation of Flk1 single positive HVM. Results from CRISPR screening were confirmed using corresponding KO lines. Although significant research efforts have been devoted to studying factors involved in the formation of hemogenic endothelium and endothelial-to-hematopoietic transition (EHT), the initial steps in mesodermal specification remain understudied. Thus, the findings are novel and significant. I have only minor comments, mostly related to some data interpretation.\n1. To assess the impact on commitment versus viability, the authors evaluated proliferative potential and viability after gene knockout. What about the possibility that certain genes might delay the maturation of the Flk1 SP population instead of abrogating it?\n2. There is some confusion regarding the phenotype used to identify the cell populations defined in the main and supplementary Fig. 1. The VSM population appears to express very high levels of endothelial/early hematopoietic markers CD43, CD44, and CD202b. This could be due to issues with cell separation by flow or the presence of the CD31+CD144- endothelial population in cultures.\n3. C8 and C7 may represent different parts (anterior-posterior) of the primitive streak based on the high expression of MIXL1, EOMES, MESP1, LHX1, and TLL1 genes found in the primitive streak. It could be useful to check/show additional primitive streak markers, Meox, GATA6, SOX17 and FOXA2, to see if these populations reflect different parts of the primitive streak. C2 and C3 most likely represent a lateral plate mesoderm since cells express very low levels of T and high levels of the lateral plate mesoderm markers FOXF1, HAND1, and BMP4.\n4. I would discourage using the term “mature endothelial cells” to define C9 cluster cells. These emerging endothelial cells co-express KDR and likely lack arterial and venous markers, thus representing early endothelial progenitors or emerging endothelial cells rather than mature endothelial cells. They also express high levels of the PLXND1 gene, which is involved in cardiovascular development, and could be endothelial cells developing from the anterior primitive streak committed to cardiac fate.\n5. Table 1 is in supplemental materials and, therefore, should be labeled as Table S1. Otherwise, it is challenging to locate this Table.", "reviewer_3": "In this manuscript, the authors perform a CRISPR-Cas9 KO screen in a murine embryonic stem cell (ES) system to identify novel regulators of haemato-endothelial mesoderm differentiation. Upon identifying potentially interesting candidates in their screen, they apply selection criteria to restrict the number of hits down to 4 genes and proceed to generate stable KO ES cell lines. They next combine spectral and classic flow cytometry with single cell RNA sequencing to characterize the effect of genetic deletion of the hit genes during haemato-endothelial differentiation.\nThe methodologies in this study are generally solid and the experiments appear to be well designed and executed (although lacking in reporting detail – see below). However, I am not convinced that the data reported in this manuscript can address the main questions that the authors would like to answer. Quoting from the introduction, lines 88-90: “The major open question in the field today is how these - and possibly other yet unidentified - regulators cooperate to allow the initial transition towards the blood lineage by the differentiation of priMes to the HEM”. Although some transcription factors potentially involved in haemato-mesoderm formation are identified here, there is no data about how these genes cooperate with existing factors, as only single KO lines are analyzed; no molecular studies are performed to evaluate how the identified TFs interact. Furthermore, I find the title of the MS “Targeted CRISPR-Cas9 screening identifies transcription factor network controlling murine haemato-endothelial fate commitment” misleading as in this manuscript no analysis of transcription factor networks is provided.\nOf the four validated genes, two were previously known as regulators of hemato-endothelial differentiation (Ldb1 and Smad1, refs. 35 and 36 and others). The other gene validated as a putative positive regulator, Six4, was not previously implied in HEC differentiation, but it appears not essential for blood generation in their system (See Fig. 3b, 4a) though its KO results in a decrease in the frequency of Flk1+ mesoderm. At the same time, the KO of Zbtb7b, putative repressor of hemato-endothelial differentiation, only results in an increase of primitive erythrocytes (Figure 4), a lineage not necessarily derived by HEC, and does not result in an increase of Runx1+ HEC (Figure 5). Therefore, I find the results presented not convincing enough to justify the authors’ claims and conclusions. Moreover, some conclusions are not supported by the data.\nIn general, I find this work largely descriptive. It is hard to grasp the broader significance of the findings presented here when murine ES cells are the only model used throughout the manuscript, and no in vivo assays are employed. The only in vivo reference is mapping of scRNA-Seq data to an existing dataset, which provides little information other than the apparent lack of an interesting phenotype for Six4 and Zbtb7b KO, which are very similar to the WT control (Supp Figure 4). Specific comments below.\nMajor points.\n1. Not enough methodological details are provided to adequately describe the techniques and methodologies. Very little information is provided in figure legends or in materials and methods. For example, a “high-dimensional flow cytometry panel using antibodies recognising 23 surface markers” is established. Results were reported in Figure 1 and 4 in the form of UMAP plots only. What surface markers have been used? How were cells gated and how were different cell subsets defined (e.g. primitive and definitive subsets)?\nFigure 1c: the legend reports an “UMAP of 122 cells… coloured according to surface marker expression” but it looks like an RNA velocity analysis approach was used here, which is missing in the legend.\n2. line 138-139: “This confirmed that DP priMes indeed had less capacity to form haematopoietic and endothelial lineages compared to F_SP HEM” I do not agree with this conclusion. I think what this experiment shows is that the frequency of hematopoietic progenitors in the DP population is lower than in the SP population, but not necessarily that the progenitors have less capacity to form hemato-endothelial lineages. Can the authors quantify the frequency of hematopoietic progenitors in each subset, for example by a limiting dilution approach?\n3. I am not sure about the definition of the vSMC subset. These cells express CD43, CD14, CD44, CD16/32 (Supplementary Figure 1), strongly suggesting a hematopoietic identity instead.\n4. As mentioned by the authors, it is strange that no chromatin factors emerge in the CRISPR screen in the transition from DP priMes towards F_SP HEM. Is this due to experimental conditions of the assay or is there a biological reason for this? It would be interesting to know what is going on with the other transitions.\n5. Validation of targeting for the KO clones should be provided. More specifically, it would be important to provide a complete characterization of the KO lines (where did the editing occured? which gRNA were used? what kind of impact had on protein expression? etc.)\n6. Proper quantification of the analyzed populations in Fig3 should be provided.\nFor example, in Figure 3a-b: Are there differences in the % of P-SP cells in the cultures generated from the KOs, as it would appear by the representative data shown in the dot plots? And in the DN? These are not commented on.\nCan the authors perform hemato-endothelial culture of these subsets (along with DP priMes and F_SP HEM), followed by a simple FACS analysis of CD41 and VECad as it was done in Figure 1?\n7. How could the authors analyze EB cultures of NPM1 KO (Fig3h) when no live cells were present? Can they show dot plots for this analysis?\n8. Figure 3i: what does the scale represent exactly? It seems like there is no expression at all for Six4 and Zbtb7b. How do the authors explain this?\n9. The authors focus on the role of the selected factors on F_SP HEM commitment, but the impact on other transitions is not taken into account. Considering the broad expression of some of these factors (Fig. 3) and their known involvement in hemato-endothelial specification (i.e. Smad1), it would be interesting to know/quantify their impact on the earlier transitions.\n10. Figure 4: Why did the authors sort total Flk1+ cells and not F_SP only since they say this is the affected population?\n11. line 261-262: “Six4 KO cells showed a marked differentiation block towards the endothelial lineage”. How is blood generated in these KO cells? From the UMAP in Figure 4 it seems that primitive erythrocytes are generated as well as “preHSPC” (what are these cells? What markers do they express?) Most importantly, where is the hemogenic endothelium in these haemato-endothelial cultures? Supposedly with the 23-marker flow panel the authors could define a putative hemogenic endothelial population. Is this still detected in the KO cultures? In lines 402-404 the authors also state that “As endothelial cells are critically needed for blood formation it is tempting to speculate that Six4 is involved in the differentiation of haemogenic endothelial cells (HECs).”. Have hemogenic endothelial cells been detected in Six4 KO cultures?\n12. lines 269-272: “This result indicates that Zbtb7b KO mesoderm is already committed towards haematopoietic and endothelial fate and their enhanced capacity during haemangioblast culture could be a direct effect of the increased frequency of F_SP cells in the starting culture” How many cells were actually plated here? Given the different F_SP frequencies, can the authors repeat the experiment with equal numbers of F_SP cells at the start of the culture?\n13. lines 316-318: “Interestingly, our differentiation experiments had suggested that Six4 KO cells were able to form endothelial but not haematopoietic lineages, even if the most advanced C6 was present (Fig. 4a)”. Isn’t this a contradiction (see previous point 11 and lines 261-262)? Figure 4a clearly shows that hematopoietic cells are in fact generated in Six4 KO.\n14. The scRNA seq on Flk1+ cells presented in Figure 5 are puzzling.\n- If C5 is “characterised by the highest expression of Etv2 and additional TFs associated with haematopoietic development, including Gata3 and Lmo2” and “is clearly committed towards blood development” (lines 330-332), then why is it overrepresented in Ldb1 KO which show a complete block in haemato-endothelial differentiation? On the other hand-why in Zbtb7b KO C5 does not show a higher frequency than the WT?\n- Again on the Zbtb7b KO, these cells show an enrichment of C4 (pre-HE mesoderm) but not the more hematopoietic committed clusters C5 and C6. How do the authors explain this?\n15. lines 356-357: “Zbtb7b-KO cells, which are largely composed of the same clusters as the Ctrl, formed the most advanced C6 (HEM 2) via a different trajectory” How was this determined? Has pseudotime analysis been performed?\n16. lines 355-356: “For all analysed conditions, Zbtb7b KO mapped with the highest frequency on the in vivo trajectory (21.1% vs. 20.4% for Ctrl) Can the authors explain what this statement means exactly? Do they consider this a significant difference?\n17. I generally disagree with the authors’ conclusions regarding Smad1 KO. The fewer live cells in Smad1 KO (Figure 4c) could be indicative of a more generic role of Smad1 than the one proposed here, i.e. a more profound effect on all mesoderm. Proper quantification and statistical tests should be reported for each cell type if they want to make points such as “Smad1 KO cells were able to form both haematopoietic and endothelial cells albeit at a reduced frequency and number” (lines 264-265)\n18. Line 276, “Heterogeneity of mesodermal cells is regulated by core TF network”. I am not sure what the authors mean with this. They are not really assessing any kind of network, but just the effect of independent mutations. The impact of those mutations on gene expression or accessibility of other TFs is not analyzed.\nMinor comments\n1. line 98- typo: Ldb1 is the correct gene name.\n2. lines 150-152: “Finally, to further underline the relevance of our ESC model we mapped the in vitro-generated cells from both EB culture and haemangioblast culture onto a mouse gastrulation atlas of cells isolated from E6.5–E8.5 embryos”. What kind of data were mapped here?"}, "author_response": "Point-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\nResponse to reviewer Comments\nWe thank the reviewers for their constructive comments and valuable suggestions on our previous\nmanuscript version. We have carefully addressed all points raised in the following point-by-point response.\nTo support our revision, we provide additional experimental data, further analysis, and clarified conclusions\nthroughout the revised manuscript.\nIn particular, based on the reviewers’ recommendations, we have implemented the following key revisions:\n- New Experimental Data: We employed a newly established long-term haematopoietic assay to assess\nthe blood-forming potential and mature blood cell output of F_SP HEM versus DP priMes.\n- Extended CRISPR-Cas9 Analysis: We expanded our CRISPR-Cas9 gene ranking analysis to include\nall mesodermal transitions. This dataset may serve as a valuable resource for the broader community\ninvestigating molecular regulators of early lineage commitment.\n- Hypothesis Testing: We conducted additional experiments to rule out the possibility that the identified\nF_SP HEM driving transcription factors delay HEM maturation, thereby strengthening the functional\ninterpretation of our findings.\n- Expanded Data Analysis and Clarifications: We conducted deeper analysis of existing datasets,\nenhanced the Materials and Methods section for clarity and reproducibility, and incorporated textual\nclarifications throughout the manuscript.\nTo address these revisions, we have added updated figures/ figure panels to the revised manuscript:\n-New panels in Figure 1: d, f\n-New Supplementary Figure 2\n-New panel in Supplementary Figure 3: a\n-New panels in Supplementary Figure 4: b–d, f–k\n-New Supplementary Figure 6\n-New Supplementary Tables 1 and 2\nThe new additions and responses to the reviewers’ comments can be found in the point-by-point response\nbelow.\nReviewer #1 (Remarks to the Author):\nOverview: This manuscript reports a novel transcription factor network that drives the transition of murine\nprimitive mesoderm to hemato-endothelial mesoderm (HEM)—extending previous work that has identified\nRunx1, Gata2 (and more recently, ETV2) as key regulators of early blood lineage commitment. Using a\nCRISPR-Cas9 knockout screen that targets TFs in a mouse ESC blood differentiation model, the authors\nidentify four novel transcriptional regulators (Lbd1, Smad1, Six4, and Zbtb7b) that control lineage\nspecification from primitive toward hemato-endothelial mesoderm. Teske and colleagues subsequently\nperform loss-of-function studies by knocking out each TF individually, which perturbs normal hematopoiesis\nin vitro, thus validating these genes as functional regulators of hemato-endothelial lineage commitment.\nTaken together, this technically rigorous screen resulting in identification of a novel TF network crucial for HE\ncommitment advances our understanding of the complex networked regulators of murine hematopoiesis,\nwhich will inform ongoing and future efforts to generate hematopoietic cells from pluripotent stem cells (in\nparticular, because generation of specific blood lineages hinges upon modulation of distinctly patterned\nsubsets of mesoderm at the earlies stages of embryonic development).\nSummary of study: Teske and colleagues first characterize and validate an in vitro EB-culture mESC blood\nhaemato-endothelial fate commitment”\ndifferentiation model, based off prior protocols by Gordon Keller’s and Georges Lacaud’s groups. They\ndescribe the progressive formation of distinct mesodermal entities defined by FLK1 and PDGFRa, where\ncells progress from populations that are (1) double-negative to (2) PDGFRa single-positive to (3) double-\npositive to (4) FLK1 single-positive, consistent with mesodermal commitment. RNA-seq reveals progressive\ndownregulation of pluripotency markers and upregulation of mesodermal and hemato-endothelial genes\nalong this developmental trajectory, while FLK1-single positive populations exhibit increased developmental\npotential for blood lineages (assayed for CD41 and VE-Cadherin).\nNext, the authors perform a pooled CRISPR KO screen to identify putative drivers and repressors of hemato-\nendothelial (HEM) commitment and recover a list of candidate regulators of the transition from primitive\nmesoderm (double positive) toward FLK1-single positive HEM, identifying top depleted and enriched genes.\nThe authors subsequently generate independent KO ES cell lines and differentiate them to assess relative\ncompetence to generate Flk1+ single-positive mesoderm, validating Smad1, Ldb1, Six4 as drivers—and\nZbtb7b as a repressor—of the HE mesodermal commitment from primitive mesoderm. ETV2 was recovered\nas a top depleted hit and confirmed as a driver of primitive-to-HE mesoderm transition in accordance with\nprevious studies, serving as a positive control.\nTo further uncover the effects of each gene KO on the downstream differentiation potential of HE mesoderm,\nthe authors drive mesoderm cells toward hematopoietic and endothelial lineages, followed by analysis of fate\nspecification, revealing that loss of function of each TF significantly biases the lineage differentiation of\nmesoderm subsets. Finally, the authors perform scRNA-seq characterization of FLK1+ cells in each KO line\nand identify 10 clusters corresponding to different developmental timepoints, finding that several clusters are\nabsent in or specific to individual TF KOs, and that frequencies are distinct across genetic perturbations.\nThrough pseudotime analysis, they construct a mesodermal developmental trajectory and demonstrate that\neach regulator TF distinctly modifies the trajectory of lineage commitment.\nMajor points:\n1. The screen is performed on the basis of loss of PDGFRa expression in FLK1+ cells as demarcating the\ntransition from primitive to hemato-endothelial mesoderm. While the authors do show in Figure 1c/d that\nFlk1+ single-positive cells are more competent to form VeCad+ CD41+ cells (and to give rise to hematopoietic\nand endothelial lineages), there are still significant numbers of cells that do not induce VeCad/CD41\nexpression at EB Day 5, suggesting that the Flk1 SP population is heterogenous. The overall significance of\nthe screen could be strengthened by molecular characterization of more mature blood cells differentiated\nfrom the FLK1+PDGFRa- (F_SP HEM) versus FLK1+PDGFRa+ (DP priMes) fractions respectively, beyond\nmerely showing that the SP fraction generates slightly higher percentages of VeCad+ and CD41+ cells. While\nthe bulk and scRNA-seq data on EB Day 5 cells is convincing in demonstrating that early mesodermal\nmarkers and hematopoietic and endothelial markers are both segregated and enriched in DP_SP and F_SP\nHEM cells respectively, further functional and molecular characterization of specific blood cell types\ndescending from these fractions of mesoderm (beyond solely flow cytometry analysis of surface markers in\nFig 1e at 2.5 days post-EB culture) would reinforce the biological significance of PDGFRa downregulation.\n(For example, demonstration of enhanced emergence of HSCs or of other cell types, from the VeCad+CD41+\nfraction arising from F_SP as compared to DP preMes (or even from the bulk F_SP HEM population as\ncompared to DP priMes) would provide further assurance that PDGFRa downregulation does serve as a\ndevelopmentally relevant transition.)\nAuthor response: We thank the reviewer for raising the important point about the biological significance of\nFLK1/PDGFRa- expression during early blood development. Below, we address this concern in detail.\nRegarding the relevance of FLK1/PDGFRa- expression in mesodermal subsets, we acknowledge their\nbiological significance and refer to several important studies demonstrating the usefulness of FLK1/PDGFRa-\nexpression in distinguishing the developmental potential of mesodermal subpopulations (PMID: 21911838;\nPMID: 23335233: PMID: 9493833). The primary goal of our study was to identify factors regulating HEM\ndifferentiation and not on molecular regulators impacting mature blood cells development. That said, we\nhaemato-endothelial fate commitment”\nagree with the reviewer that it is a very important and interesting question whether mesodermal\nsubpopulations differ in their potential in generating blood, which partially has been previously addressed in\nother studies (PMID: 21911838; PMID: 23335233: PMID: 9493833).\nTo address this concern in our study, we adapted a long-term haematopoietic assay in liquid culture (based\non methods from PMID: 19182774; PMID: 19277585) supporting the maturation of haematopoietic\nprogenitors and compared F_SP HEM and DP priMes cells in terms of (i) their blood forming potential\nand (ii) their ability to generate mature blood lineages. Defined numbers of FACS-sorted F_SP HEM or\nDP priMes cells (18k, 10k or 5k) from EB cultures were plated in gelatine-coated 96-well plates. A minimum\nof 24 wells were seeded for each condition. Cells were cultured first 2.5 days in haemangioblast culture\nmedia (to support the generation of haematopoietic progenitors) and subsequently in media supplemented\nwith SCF, IL-3, G-CSF, IL-11, IL-6, TPO, M-CSF and erythropoietin to support the maturation of\nhaematopoietic progenitors. After 9.5 days in culture, wells were scored for haematopoietic cells (REV\nFig.1A, B). REV Fig1. A shows representative microscopy images of a positive culture containing\nhaematopoietic cells started from F_SP HEM and negative culture started from DP priMes where no\nhaematopoietic cells formed at day 9.5. Whereas F_SP HEM generated haematopoietic cells under all tested\nconditions, DP priMes showed reduced blood-forming potential, particularly at lower cell inputs: For 5k cells:\n100% of F_SP HEM formed colonies, but only 8% of DP priMes (REV Fig. 1B). Additionally for blood lineage\nanalysis, we scored 8 randomly selected wells for each condition and analysed the number of live cells and\nthe frequency of leukocytes (CD45+) and erythroid (CD71+) cells, the two main blood lineages forming in the\nassay (REV Fig. 1 C-F). Please see REV. Fig 1C for a representative flow cytometry analysis of CD45 and\nCD71 surface expression of a positive culture started from F_SP HEM (upper) and negative culture started\nfrom DP priMES (lower). F_SP HEM consistently showed higher number of live cells and increase\nfrequencies of CD45+ and CD71+ cells in all analysed conditions compared to DP priMes (REV Fig. 1D-F).\nCollectively, these results demonstrate that mesodermal populations, defined by FLK1/PDGFRα expression,\ndiffer significantly in their blood-forming capacity. F_SP HEM is functionally distinct from DP priMes with\nsuperior potential to form mature haematopoietic cells due to increased potential to form haematopoietic\nprogenitors. This highlights the importance of FLK1/PDGFRα expression in marking functionally\ndistinct mesodermal subpopulations during early haematopoietic development. This data is now\nincluded in our manuscript as Supp. Fig 2.\nREV Fig.1\n125\n100\n75\n50\n25\nF\n8\n_\nk SP 10k DP 10\nF\nk _SP 5k DP 5 k\n2. In this study, characterization of in vitro differentiated hemato-endothelial lineages (HB culture) is\nperformed at 2.5 days past FACS isolation of Day 5 EBs. The developmental significance of the authors’\n3\nlaitnetop\ndoolb\n%\n18k\nlaitnetop\ndoolb\n%\nF_SP HEM\nDP priMes\n10k 5k\nsllec\nevil\n#\n+54DC\n%\n+17DC\n%\nB\nD E F\nF_SP HEM F_SP HEM F_SP HEM 80000 DP priMes DP priMes DP priMes\n60000\n40000\n20000\nsllec\nevil\n#\n60\n40\n20\n+54DC%\n100\n80\n60\n40\n20\n+17DC%\nA\n104\nplated cells 103\n0\n-103 CD71pos 1.43\n0 104 105 Comp-YG 780_60-A :: CD71\n18k 10k 5k 18k 10k 5k 18k 10k 5k plated cells plated cells plated cells\n54DC :: A-05_017\neulB-pmoC\n250K\n200K\n150K\n100K\n50K live 4.85 0 0 50K 100K 150K 200K 250K\nFSC-A\nA-CSS\n104\n103\n0\n-103 CD 5 7 1 1 .0 pos\n0 104 105 Comp-YG 780_60-A :: CD71\n54DC :: A-05_017\neulB-pmoC\n250K\n200K\n150K\n100K\n50K live 47.3 0 0 50K 100K 150K 200K 250K\nFSC-A\nA-CSS\n104\n103\n0 -103 CD71pos 1.43\n0 104 105\nComp-YG 780_60-A :: CD71\n54DC\n:: A-05_017\neulB-pmoC\n250K\n200K\n150K\n100K\n50K\nlive\n4.85\n0\n0 50K 100K 150K 200K 250K\nFSC-A\nA-CSS\n104\n103\n0 -103 CD 5 7 1 1 .0 pos\n0 104 105\nComp-YG 780_60-A :: CD71\n54DC\n:: A-05_017\neulB-pmoC\n250K\n200K\n150K\n100K\n50K live\n47.3\n0\n0 50K 100K 150K 200K 250K\nFSC-A\nA-CSS\n54DC\nC\n20x 20x 28\n51\n0\n0\n*** ** **** **** CD71 ****\n*** **** ***\n*\nPoint-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\nfindings could be significantly strengthened if they examined functional effects of modulating any of the four\nTFs on blood cells further differentiated down their developmental trajectory, which would lend credence to\nbiological and functional implications of perturbing early mesoderm patterning for hematopoiesis. (For\nexample, some functional effects of Zbtb7b KO on EB-derived cells after more than 2.5 days of HB culture\nwould be interesting, and it would be especially of relevance to understand if knock-out of Zbtb7b might skew\ndifferentiation or enhance the production of certain more mature blood lineages.)\nAuthor response: Is not disclosed publicly.\n3. Moreover, because there remains incomplete understanding of how faithful the in vitro differentiation of\nmurine ES cells mimics hematopoiesis in vivo, a limitation of the study is validation of the TFs in the context\nof murine hematopoiesis. While generating knockouts and evaluating hematopoiesis in murine embryos is\nbeyond the scope of this study, some discussion of the limitations of the study for revealing in vivo\nmechanisms of hematopoiesis is warranted.\nAuthor response: We thank the reviewer for raising this important point and agree with the suggestion. We\ninclude a new paragraph in the discussion of our revised manuscript addressing this limitation of the study\nas follows:\nLines 496-504:\nFurthermore, our approach of modelling early haematopoiesis ex vivo employing murine ESCs enabled us\nto obtain sufficient cell numbers for large-scale genetic screenings with the optimal sgRNA representation of\na delicate developmental time window that would have been challenging if not impossible in vivo. Still, our\nstudy would be strengthened by in vivo characterisations and experiments addressing the function\nof the identified core TFs during early mesodermal and blood commitment in mouse development.\nFor these in vivo experiments, factors that lead to early embryonic lethality like Etv2, Ldb1 and Smad1\nwould have to be contrasted to factors that have not been implicated in early haematopoiesis or have\nbeen associated with impaired survival, like Six4 and Zbtb7b.\n4. While not necessarily a pivotal issue with regard to the validity of the screen, it would strengthen the claim\nthat each TF is specifically a negative or positive regulator of mesodermal commitment if authors could\ndemonstrate that at least one of the KO phenotypes could be rescued by reintroducing the missing TFs\nexogenously through a lentiviral vector, etc.\nAuthor response: We thank the reviewer for this suggestion. To answer if manipulating the expression of one\nof the identified TFs could rescue the observed KO phenotypes we performed different sets of experiments,\ndescribed below:\n1. We first addressed if the block of HEM differentiation upon knock-out of Etv2, Ldb1, Six4 and Smad1 could\nbe rescues or ameliorated by additionally knocking-out Zbtb7b, as we identified that Zbtb7b knock out cells\nhad an increased potential to develop towards F_SP HEM. We therefore generated Zbtb7b DKO ESCs in\nthe individual single KO (SKO) lines by CRISPR-Cas9 targeting of Zbtb7b and generated ETV2/ Zbtb7b\nDKO, LDB1/ Zbtb7b DKO, Six4/ Zbtb7b DKO and Smad1/ Zbtb7b DKO lines, abbreviated as DKOs. We then\nperformed EB differentiation experiments to monitor the frequency of F_SP HEM and analysed in total 3\ndifferent DKO clones per genotype (REV Fig. 3 A).\nInterestingly, in the Ldb1/ Zbtb7b DKO cultures we detected a significant increases frequency of F_SP HEM\ncompared to LDB1 single KO cultures at EB day5 (REV Fig. 3A). The frequency of F_SP HEM in Ldb1/\nZbtb7b DKO cells was very similar to the WT level (REV Fig. 3A). The phenotypes of the remaining single\nKO lines could not be rescued by additionally knocking-out Zbtb7b.\nWe furthermore performed haemangioblast cultures of FLK1 + mesodermal cells isolated from WT, LDB1\nKO and Ldb1/ Zbtb7b DKO EB cultures to test if we could obtain endothelial/haematopoietic cells and\ntherefore rescue the developmental block of knocking out Ldb1. Despite the increased frequency of F_SP\nHEM in LDB1/ Zbtb7b DKOs cells, the cells could not form haematopoietic cells equally to the LDB1 single\nKO (REV Figure 3 B). In a follow-up, we plan to address (i) if an interaction of LDB1 and ZBTB7b exists and\nhaemato-endothelial fate commitment”\n(ii) the identity/ developmental potential of F_SP HEM cells developing in the LDB1/ Zbtb7b DKO\nbackground.\nREV Fig. 3\n40\n30\n20\n10\n2. As a second set of experiments we performed ETV2 overexpression experiments in the WT control and\nindividual KO cell lines (Ldb1, Six4 and Smad1) and aimed to address if adding back ETV2 will rescue their\ndifferentiation block towards F_SP HEM. As the reviewer suggested, we used a lentiviral approach and\ncloned the coding sequence of ETV2 in a lentiviral construct under the PGK promoter containing a\nPGK_puromycin_T2A construct. This set-up enables a puromycin selection after lentiviral transduction,\nwhich ensured ETV2 expression of the transduced cells surviving the selection.\nWe next performed EB culture experiments after ETV2 transduction and selection. Unfortunately, ETV2\noverexpression was not very well tolerated by our lines and we obtained significantly less live cells after EB\n5 days regardless of the underlying genotype (REV Fig. 4A). In addition, we detected no decrease of F_SP\ncells for the analysed phenotypes. We still continued with this not ideal EB cultures and sorted total FlK1+\ncells from both the untransduced controls and ETV2-add back cells lines and performed haemangioblast\nculture to analyse CD41 (haematopoietic marker) and VE-cad (endothelial marker) expression. We analysed\nthe potential of the add-back lines to generated endothelial and haematopoietic cells. We detected no\ndifference upon ETV2 overexpression in the Ctrl cells, but interestingly we detected partial rescues in all\nother analysed KO cell lines upon Etv2 overexpression. The rescue upon ETV2 overexpression on\nendothelial and/ or haematopoietic cell differentiation varied in the distinct KO lines. ETV2 KO cells upon\nETV2 add-back were able to differentiate to haematopoietic and endothelial cells, albeit at a very reduced\nlevel. Ldb1 KO cells +Etv2 could only generate endothelial but no haematopoietic cells. Six4 KO+ Etv2 cells\ncould reach Ctrl levels of haematopoietic cell differentiation and a marked increase of endothelial cells.\nSmad1 KO cells generated few haematopoietic cells but markedly more endothelial cells than wild type\ncontrols upon ETV2 overexpression. Collectively, despite some technical problems we could detect partial\nrescues in our KO cell lines. For the future we will now change our overexpression system and move to an\ninducible overexpression system and include additional promoters besides PKG.\nCollectively, we hope that we could address some of the concerns of the reviewer and hope to optimise our\noverexpression set-up for future studies. Unfortunately, we realised that addressing potential rescue and\nadd-back phenotype will require a substantial longer time frame.\n%\nMEH\nPS_F\n%\nFigure 3\nA B\nWT LDB1KO LDB1/ Zbtb7b DKO\n14DC\nQ1 Q2\n20.2 7.29\n104\n103\n54.1 18.4\n0 103 104\nVeCad Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\nQ1 Q2\n0.40 0.034\n104\n103\n98.8 0.79\n0 103 104\nComp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\nQ1 Q2\n0.032 0.013\n104\n103\n99.7 0.24\n0 103 104\nComp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n*\n20 7 0 0 0 0\n18 0.5 0.5\nPoint-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\nREV Fig. 4\nFigure 4\nA B\n6×107\n4×107\n2×107\n0 untransduced transduced\nMinor points:\n1. While the authors quantified sgRNAs in the T0 ESCs and were thus able to identify differences in guide\nabundance between DP and FLK1+ single-positive mesoderm, it would be informative and strengthen the\nrobustness of this specific screening platform to show that sgRNA representation in the transduced mESCs\n(post-selection for Cas9-GFP+ cells) mirrors the representation of the EpiTF plasmid library, which would\ndemonstrate that sgRNAs were introduced into ESCs without significant biases (e.g., a simple Lorenz curve\nin the supplement to show sgRNAs are uniformly expressed in ESCs without biases).\nAuthor response: We thank the reviewer for raising this important point regarding sgRNA representation and\napologise for not including this information in our initial manuscript.\nTo directly assess sgRNA representation post-transduction, we have added a new panel (now included as\nSupplementary Figure 3a/ REV Fig. 5) comparing the composition of the initial CRISPR plasmid library with\nthe sgRNA abundance in FACS-sorted GFP⁺ ESCs at the start of differentiation (48 hours post-transduction).\nWhile the histogram for the T0 ESC sample shows a slightly broader distribution than the plasmid library, this\nvariation does not indicate any major skew or dropout. The overall sgRNA representation remains\ncomparable, with no significant biases detected after ESC transduction. Additionally, to further ensure robust\nrepresentation, we performed all screens with a high coverage of approximately 500 x per guide thus\nminimizing the risk of losing low abundance sgRNAs and enhancing the reliability of downstream results.\n6\nsllec\nevil\n#\nsllec\nevil\n#\n104Q311.0 Q02\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.1 4.Q992\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS1 2 1in_ 3g5le_ CCterl_lls001.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q1.100 0.Q182\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS4 2 2in_ 2g5 6le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q4.132 2.Q262\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS2 2 2in_ 0g5 1le_ ECTeVll2s KO_003.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS1 2 4in_ 1g5 3le_ ECTeVll2s KO_ETV2ab_004.fcs\n104Q0.123 0.Q232\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.148 0.Q242\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS4 2 3in_ 1g5le_ LCDeBll1s KO_005.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.2 7.Q462\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS1 2 2in_ 5g5 3le_ LCDeBll1s KO_Etv2ab_006.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q151.3 2.Q212\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\ndS9 2 3in_ 0g5 9le_ SCixe4ll sKO_Etv2ab_008.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS3 2 4in_ 8g5 0le_ SCixe4ll sKO_007.fcs\n104Q6.170 1.Q682\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.162 0.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 5in_ 8g5le_ SCmeallsd1KO_Etv2ab_010.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q341.4 5.Q442\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 4in_ 5g5 7le_ SCmeallsd1KO_009.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q311.0 Q02\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 3in_ 4g5 3le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.1 4.Q992\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 1in_ 3g5le_ CCterl_lls001.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q1.100 0.Q182\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 2in_ 2g5 6le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q4.132 2.Q262\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS2 2 2in_ 0g5 1le_ ECTeVll2s KO_003.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS1 2 4in_ 1g5 3le_ ECTeVll2s KO_ETV2ab_004.fcs\n104Q0.123 0.Q232\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.148 0.Q242\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 3in_ 1g5le_ LCDeBll1s KO_005.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.2 7.Q462\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 2in_ 5g5 3le_ LCDeBll1s KO_Etv2ab_006.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q151.3 2.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS9 2 3in_ 0g5 9le_ SCixe4ll sKO_Etv2ab_008.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS3 2 4in_ 8g5 0le_ SCixe4ll sKO_007.fcs\n104Q6.170 1.Q682\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.162 0.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 5in_ 8g5le_ SCmeallsd1KO_Etv2ab_010.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q341.4 5.Q442\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 4in_ 5g5 7le_ SCmeallsd1KO_009.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q311.0 Q02\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 3in_ 4g5 3le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.1 4.Q992\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 1in_ 3g5le_ CCterl_lls001.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q1.100 0.Q182\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 2in_ 2g5 6le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q4.132 2.Q262\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS2 2 2in_ 0g5 1le_ ECTeVll2s KO_003.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS1 2 4in_ 1g5 3le_ ECTeVll2s KO_ETV2ab_004.fcs\n104Q0.123 0.Q232\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.148 0.Q242\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 3in_ 1g5le_ LCDeBll1s KO_005.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.2 7.Q462\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 2in_ 5g5 3le_ LCDeBll1s KO_Etv2ab_006.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q151.3 2.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS9 2 3in_ 0g5 9le_ SCixe4ll sKO_Etv2ab_008.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS3 2 4in_ 8g5 0le_ SCixe4ll sKO_007.fcs\n104Q6.170 1.Q682\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.162 0.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 5in_ 8g5le_ SCmeallsd1KO_Etv2ab_010.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q341.4 5.Q442\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 4in_ 5g5 7le_ SCmeallsd1KO_009.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q311.0 Q02\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 3in_ 4g5 3le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.1 4.Q992\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 1in_ 3g5le_ CCterl_lls001.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q1.100 0.Q182\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 2in_ 2g5 6le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q4.132 2.Q262\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS2 2 2in_ 0g5 1le_ ECTeVll2s KO_003.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS1 2 4in_ 1g5 3le_ ECTeVll2s KO_ETV2ab_004.fcs\n104Q0.123 0.Q232\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.148 0.Q242\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 3in_ 1g5le_ LCDeBll1s KO_005.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.2 7.Q462\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 2in_ 5g5 3le_ LCDeBll1s KO_Etv2ab_006.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q151.3 2.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS9 2 3in_ 0g5 9le_ SCixe4ll sKO_Etv2ab_008.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS3 2 4in_ 8g5 0le_ SCixe4ll sKO_007.fcs\n104Q6.170 1.Q682\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.162 0.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 5in_ 8g5le_ SCmeallsd1KO_Etv2ab_010.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q341.4 5.Q442\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 4in_ 5g5 7le_ SCmeallsd1KO_009.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q311.0 Q02\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 3in_ 4g5 3le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.1 4.Q992\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 1in_ 3g5le_ CCterl_lls001.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q1.100 0.Q182\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 2in_ 2g5 6le_ CCterl_llsETV2add-back_002.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q4.132 2.Q262\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS2 2 2in_ 0g5 1le_ ECTeVll2s KO_003.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS1 2 4in_ 1g5 3le_ ECTeVll2s KO_ETV2ab_004.fcs\n104Q0.123 0.Q232\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.148 0.Q242\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS4 2 3in_ 1g5le_ LCDeBll1s KO_005.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q331.2 7.Q462\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 2in_ 5g5 3le_ LCDeBll1s KO_Etv2ab_006.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q151.3 2.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS9 2 3in_ 0g5 9le_ SCixe4ll sKO_Etv2ab_008.fcs\n14DC\n:: A-21_556\noiV-pmoC\ndS3 2 4in_ 8g5 0le_ SCixe4ll sKO_007.fcs\n104Q6.170 1.Q682\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n104Q0.162 0.Q212\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS3 2 5in_ 8g5le_ SCmeallsd1KO_Etv2ab_010.fcs\n14DC\n:: A-21_556\noiV-pmoC\n104Q341.4 5.Q442\n103\n0 103 104 Comp-Red 670_30-A :: VeCad\ndS1 2 4in_ 5g5 7le_ SCmeallsd1KO_009.fcs\n14DC\n:: A-21_556\noiV-pmoC\n34 5 33 5 1 o 4 2\n32 29 1 7\n0 0 0 0 15 2 33 7\n1 5 7 15\n0 0 7 2\n2 50\ndS3 2 3in_ 4g5 3le_ CCterl_llsETV2add-back_002.fcs\n14DC\nEtv2 KO +Etv2\nVeCad\nPoint-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\nFiguRre E4 (nVew FSuipgple.m5e ntary Figure 2 b)\nA\n800\n600\n400\n200\n0\n0 50 100 150\ncounts per million\n2. Typo on page 9, line 234: “but was broadly expressed in all populations; while Ldb1, Six4.”\nAuthor response: The typo was corrected.\n3. Typo on page 11, line 346: “sort due to a less stringent gating strategy.”\nAuthor response: The typo was corrected.\n4. Page 11, line 342 “Supp Fig 5b” should instead be “Supp Figure 4b.”\nAuthor response: The typo was corrected.\nReviewer #2 (Remarks to the Author):\nIn this manuscript, Teske et al. describe the identification of novel transcription factors involved in specifying\nhematovascular mesoderm (HVM). Using CRISPR/Cas9 screening, authors revealed that Smad1, Ldb1,\nSix4, and Zbtb7b had a major impact on the formation of Flk1 single positive HVM. Results from CRISPR\nscreening were confirmed using corresponding KO lines. Although significant research efforts have been\ndevoted to studying factors involved in the formation of hemogenic endothelium and endothelial-to-\nhematopoietic transition (EHT), the initial steps in mesodermal specification remain understudied. Thus, the\nfindings are novel and significant. I have only minor comments, mostly related to some data interpretation.\n1. To assess the impact on commitment versus viability, the authors evaluated proliferative potential and\nviability after gene knockout. What about the possibility that certain genes might delay the maturation of the\nFlk1 SP population instead of abrogating it?\nAuthor response: We thank the reviewer for raising this important point regarding a potential delayed\nappearance of F_SP HEM during EB cultures in the identified TF KOs.\nThe kinetics of FLK1 expression in embryoid body (EB) differentiation of ESCs were first established in the\nseminal work of Gordon Keller’s group (PMID: 9169850). FLK1 expression exhibits a dynamic and narrow\nwindow, peaking around day 4–5 and declining rapidly thereafter. Haemato-endothelial progenitors are\nknown to arise during this transient FLK1 high phase. Consistent with these findings, we observe the highest\nfrequency of F_SP HEM at the FLK1 expression peak (day 5 in our system, please see Fig. 1a, b of the\nmanuscript, REV Fig.6 A, B). This timing is critical, as delaying analysis risks missing this window of haemato-\nendothelial competence. To rule out the possibility of delayed F_SP HEM emergence in the KO lines, we\nextended EB differentiation to day 6 and repeated our analysis. Across all genotypes, total FLK1 and F_SP\nHEM frequencies were consistently lower on day 6 compared to day 5 (REV Fig. 6 A–C), suggesting that the\ndifferentiation potential had already declined. We additionally performed haemangioblast cultures of FLK1+\n7\nycneuqerf\n800\n600\nPlasmid\nT0\n200\n0\n0 50 100 150\ncounts per million\nycneuqerf\nPool\nPPlalasmsmid ildibrary\nTTra0nsduced ESC T0\nycneuqerf\nCounts per Million\nPoint-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\ncells isolated from day6 (REV Fig. 6 D). No increase in haematopoietic or endothelial potential was detected\ncompared to day 5 and we confirmed our KO line-specific observations: Etv2 KO and Ldb1 KO, showed a\ncomplete block in haemato-endothelial differentiation; Six4 KO and Smad1 KO, retained limited capacity to\nform haematopoietic and endothelial cells, though reduced compared to Ctrl cultures (REV Fig. 6D).\nCollectively, our data do not support a delayed emergence of HEM in any of the KO lines tested. Rather, the\ndata indicate a disruption or block in differentiation, not a shift in timing. We included this new data in the\nrevised manuscript (Supp. Fig 4 f-h).\nREV Fig.6\nREV Fig.6\nA\n2. There is some confusion regarding the phenotype used to identify the cell populations defined in the main\nand supplementary Fig. 1. The population appears to express very high levels of endothelial/early\nhematopoietic markers CD43, CD44, and CD202b. This could be due to issues with cell separation by flow\nor the presence of the CD31+CD144- endothelial population in cultures.\nAuthor response: We thank the reviewer for the thoughtful comment and the opportunity to clarify our\nclassification of VSM cells in Fig. 1e and the updated Fig. 1f (previously Supplementary Fig. 1f). We also\nrefer to Reviewer 3 (Q4) that raised a similar concern.\nOur high-dimensional FACS panel was primarily designed to distinguish haematopoietic and endothelial\nlineages. The identification of VSM cells, which represent an alternative differentiation fate in haemangioblast\ncultures (PMID: 11081514; PMID: 15677567; PMID: 12569129; PMID: 17084363), is based on exclusion of\nknown lineage markers. Currently, there are no widely accepted surface markers that can uniquely define\nVSM cells by FACS. As such, we identified the VSM population indirectly, using negative gating -specifically,\nby excluding cells expressing endothelial and haematopoietic lineage markers. For any cluster to be bona\nfide haematopoietic a particular focus is on CD41 expression, the most specific and reliable marker for early\n8\n1KLF\nEB day6\nPDGFRa\nD\nBC day2.5\n14DC\nCtrl Etv2 KO Ldb1 KO Six4 KO Smad1 KO\n103\n21.6 53.0\n0 103 104\nComp-PE-A :: PDGFRa\nCtrl Etv2 KO Ldb1 KO Six4 KO Smad1 KO\nVeCad\n1KLF\n:: A-5-5yC-PCreP-pmoC\n103\n19.9 66.0\n0 103 104\nComp-PE-A :: PDGFRa\nday6_Ctrl_001.fcs\nsinglets\n135872\n1KLF\n:: A-5-5yC-PCreP-pmoC\n103\n31.7 43.4\n0 103 104\nComp-PE-A :: PDGFRa\nday6_ETV2KO_002.fcs\nsinglets\n109752\n1KLF\n:: A-5-5yC-PCreP-pmoC\n103\n22.0 57.9\n0 103 104\nComp-PE-A :: PDGFRa\nday6_Ldb1KO_003.fcs\nsinglets\n115032\n1KLF\n:: A-5-5yC-PCreP-pmoC\n103\n46.9 30.0\n0 103 104\nComp-PE-A :: PDGFRa\nday6_Six4KO_004.fcs\nsinglets\n109977\n1KLF\n:: A-5-5yC-PCreP-pmoC\nday6_Smad1KO_005.fcs\nsinglets\n122267\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\n14DC\n:: A-21_556\noiV-pmoC\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\nd2_5_Ctrl_001.fcs\nSingle Cells\n1488\n14DC\n:: A-21_556\noiV-pmoC\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\nd2_5_Etv2KO_002.fcs\nSingle Cells\n2221\n14DC\n:: A-21_556\noiV-pmoC\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\nd2_5_Ldb1KO_003.fcs\nSingle Cells\n1157\n14DC\n:: A-21_556\noiV-pmoC\n103\n0 103 104\nComp-Red 670_30-A :: VeCad\nd2_5_Six4KO_004.fcs\nSingle Cells\n1660\n14DC\n:: A-21_556\noiV-pmoC\n6 19 2 12 7 18 4 16 9 12\n80\n60\n40\n14 4 1 0 1 0 9 1 2 0\n26 0 1 5 11\nd2_5_Smad1KO_005.fcs\nSingle Cells\n133\n+1KLF\nlatot\n%\n60\n40\n1kLF\nlatot\n%\nPS_F\n%\nMEH\nPS_F\n%\n27 39 1 47 4 54 8 51 2 44\nEB day5\nB C\nday5 day6 day5 day6\nPoint-by-point response; Teske et al. “Targeted CRISPR-Cas9 screening identifies core transcription factors controlling murine\nhaemato-endothelial fate commitment”\nblood development (PMID: 12900455; PMID: 11934866). For any cluster to be assigned an endothelial\nidentify (or cells undergoing EHT) a particular focus lies on VE-cad, the most specific and reliable marker for\nendothelial cells (PMID: 8555485). The VSM cluster is VE-cad neg/ CD41 neg, supporting a non-\nhaematopoietic, non-endothelial identity. Further evidence comes from the analysis of haemangioblast\ncultures from EVT2 KO cells, which also differentiate into cells of the VSM cluster (Fig. 4 a, b). ETV2 is the\nMaster TF, needed for endothelial and haematopoietic differention. We therefore do not consider a\nhaematopoietic/ endothelial identity of the VSM cell cluster at the moment.\nWe recognize that the VSM cluster expresses some markers commonly associated with haematopoietic\ncells, including CD44, CD202b (Tie2), CD43, CD14, and CD16/32. However, expression of these markers\nis not exclusive to the haematopoietic lineage: CD44 has been reported to be expressed on VSM cells (PMID:\n8609213). Additionally, CD202b (Tie2) expression was detected in embryonic VSM precursors and primitive\nmesoderm cells, prior to fate commitment (PMID: 16926294"}]

Decision letter