In vivo CRISPR screening identifies SAGA complex members as key regulators of hematopoiesis

Domain: neuroscience
Published: 2026-01-23
Editorial outcome: major_revision

Abstract
The biological mechanisms that sustain the vast blood production required for healthy life remain incompletely understood. To search for cell intrinsic regulators of hematopoiesis, we perform a genome-wide in vivo hematopoietic stem and progenitor cell (HSPC)-based CRISPR knockout screen. We discover SAGA complex members, including Tada2b and Taf5l, as key regulators of hematopoiesis. Loss of Tada2b or Taf5l strongly inhibits hematopoiesis in vivo, causing a buildup of immature hematopoietic cells in the bone marrow. The SAGA complex deposits histone H3 lysine 9 acetylation (H3K9ac) and removes histone H2B ubiquitination (H2Bub). Loss of Tada2b leads to a reduction in H3K9ac levels and altered H2Bub enrichment in HSPCs, implicating disruption of SAGA complex activity. This is associated with upregulation of interferon pathway genes, reduced mitochondrial activity, and increased megakaryocyte progenitor cell commitment. Loss of these factors also enhances the cell outgrowth and the interferon pathway in an in vivo human myelodysplastic syndrome cell line model. In summary, this study identifies the SAGA complex as an important regulator of hematopoiesis.

Introduction
Hematopoietic stem cells (HSCs) are a rare bone marrow (BM) cell type that have the potential to self-renew or undergo lineage commitment and differentiate into one of the many hematopoietic and immune cell types via a range of intermediate hematopoietic progenitor cells (HPCs)1–5. As such, hematopoietic stem and progenitor cells (HSPCs) support the hematopoietic and immune systems throughout life, which play various essential roles in human health from oxygen supply to wound healing to defense against pathogens. Approximately 1011 new blood cells are generated per day in humans. Hematopoietic system function generally declines with age and can result in leukopenia, anemia, myelodysplastic syndrome (MDS), and leukemia, as well as other pathologies (e.g., atherosclerosis, osteoporosis)6,7. It is also one of the major drivers of declining immune cell function in aged individuals, predisposing aged individuals to infection and cancers8. Understanding the mechanisms of healthy and pathogenic hematopoiesis is therefore a key question in the field of hematology, stem cell biology, and gerontology. In particular, the regulation of gene expression is critical for healthy hematopoiesis9,10. However, there is still much to be explored about this process.

Tight regulation of gene expression is critical for HSPC function, with perturbations known to underlie the loss of hematopoietic system homeostasis9. The SAGA complex is an evolutionary-conserved multiprotein complex that contains both histone acetyl-transferase and histone deubiquitinase activities11,12. The canonical enzymatic proteins are KAT2A and USP22, respectively. Loss of Kat2a has previously been shown to minimally impact HSC activity13, perhaps due to redundancy with Kat2b, although it is important in leukemic stem cell activity14. Loss of Usp22 has recently been shown to induce emergency myelopoiesis15. Alongside these enzymatic subunits, the SAGA complex contains several structural components including transcriptional adapters (TADAs) including TADA2B and TADA1, and transcription-associated factors (TAFs) including TAF5L and TAF6L. The role of these structural components in HSPC activity is poorly understood.

While HSPC-derived hematopoiesis is a well characterized system, the application of methods to identify genetic regulators of this process through high-throughput genetic screens have been hampered by the rarity of these cells. In vivo pooled CRISPR/Cas9 knockout (KO) screens offer an efficient method to investigate thousands of genetic regulators for a range of biological processes16, and have already been applied to a number of cell lines14,17,18. However, the application of this system to primary HSPCs is considerably more challenging because of the large numbers of cells required to perform these screens19. To date, in vivo HSPC CRISPR screens have been limited to single guide RNA (sgRNA) libraries targeting 30–150 genes20,21. However, recent advances in polymer-based ex vivo HSC expansion now enable large numbers of HSPCs to be generated, albeit a heterogeneous mixture of HSCs and HPCs (with transplantable HSCs at a frequency of ~1:34)22–24. Here, we have utilized this ex vivo HSPC expansion methodology to undertake a genome-wide HSPC CRISPR screen, from which we identify and validate structural components of the SAGA complex as key regulators of hematopoiesis.

Results
In vivo CRISPR screen identifies SAGA complex members as hematopoiesis regulators
To gain insight into the genetics of hematopoiesis, we optimized a large-scale ex vivo HSPC expansion culture method22–24 to generate sufficient HSPC numbers for CRISPR KO screens. We performed pooled CRISPR KO screens targeting the entire mouse genome (10 sgRNAs/gene) in 3-week expanded primary mouse HSPC cultures. To identify molecular regulators of in vivo hematopoiesis, we transplanted these gene KO HSPCs into 65 lethally irradiated recipients (Fig. 1a), achieving high-level donor peripheral blood (PB) chimerism (Supplementary Fig. S1a). After 10–12-weeks, BM and spleen from these mice were collected pooled, and various hematopoietic cell types purified for sgRNA sequencing: c-Kit+ Lineage- HSPCs, CD11b/Gr1+ myeloid cells, CD4/CD8+ T cells, B220+ B cells, and Ter119+ erythroid cells. We could detect an average of 5 sgRNA/gene for all genes in the mouse genome (Supplementary Fig. S1b, c).

In comparing sgRNA abundance between HSPCs and all mature lineage populations, gene hits included well-characterized hematopoiesis regulators such as Runx1, Gata3, and Cic (Fig. 1b, Supplementary Fig. S1f–l, Supplementary Data 1). Loss of the transcription factor Runx1 in adult HSCs has been previously shown to inhibit lymphopoiesis while enhancing myelopoiesis25. In our pairwise analysis however, we observed relative loss of Runx1 sgRNAs in all lineages including myeloid26,27 (Supplementary Fig. S1f–i). To better understand this apparent discrepancy, we generated Runx1 KO HSPCs via electroporation (Supplementary Fig. S1m, n) and transplanted them into lethally-irradiated recipients. Consistent with our screen results (Supplementary Fig. S1o), KO chimerism was higher within the c-Kit+ Lineage- bone marrow than within the T-cell, B-cell and myeloid lineages (Supplementary Fig. S1p). However, also consistent with the literature, T-cell and B-cell chimerism was low while myeloid chimerism was high after 12-weeks (Supplementary Fig. S1q). These findings help to validate our in vivo HSPC CRISPR screen and provide confidence in the hits identified by it.

One striking result of the genome-wide screen was that we identified members of the SAGA complex11 as being enriched within the HSPC population (Fig. 1b, c). These SAGA complex members (Tada2b, Tada1, Taf6l) were also seen as enriched when HSPCs were compared with each individual mature lineage (Supplementary Fig. S1f–i). To investigate this further, we performed a targeted ~2000 gene screen focused on regulators of gene expression that included many of the SAGA complex members. In this screen, we transplanted the gene expression KO library-containing HSPCs into 24 lethally irradiated recipients (Supplementary Fig. S1d, e). SAGA complex members were even more pronounced as top hits that are required for normal hematopoiesis in this focused screen (Fig. 1d–h, Supplementary Data 2).

Loss of structural SAGA complex subunits block normal hematopoiesis
Focusing further on the SAGA complex, the screens identified a specific subset of unique structural components of the SAGA complex: Tada2b, Tada1, Taf5l and Taf6l (Supplementary Fig. S2a, b). The role of these components in hematopoiesis is poorly understood. Other SAGA complex members, including enzymatic subunits (Kat2a, Kat2b, Usp22) and those not unique to the SAGA complex (e.g., Tada3 and Sgf29), were not identified as hits (Supplementary Fig. S2a, b). To validate these structural SAGA complex members as hematopoiesis regulators, we knocked out Tada2b, Taf5l, Tada1, or the Rosa26 locus (as a control) in cultured HSPCs and transplanted them into recipient mice (Fig. 2a). Gene KOs were confirmed at the DNA and protein levels (Supplementary Fig. S2c–e). Tada2b, Taf5l, and Tada1 KO cells all engrafted at much higher levels within the immature BM HSPC compartments as compared with the mature PB, while control sgRNA cells displayed similar chimerism levels throughout the hematopoietic hierarchy (Fig. 2b–g). We observed a striking increase in the frequency of immature hematopoietic cell populations (Lineage- cells and KSL HSPCs) within the KO BM cells (Fig. 2b, c). Notably, while higher levels of KO chimerism were observed in Lineage- progenitor populations, chimerism within the most primitive immunophenotypic CD150+CD34-KSL LT-HSC population was similar to the PB (Fig. 2d–g). Corresponding with the screen, SAGA component KO contribution to mature immune lineages was significantly reduced relative to HSPCs (Fig. 2h). Additionally, we confirmed that the inhibited hematopoiesis phenotype was transplantable by performing secondary transplantation assays (Supplementary Fig. S2f).

To confirm that these results are not confounded by extended time in ex vivo culture, we further validated that SAGA complex members altered the activity of functional HSCs in knock outs of Tada2b or Taf5l (using two different sgRNAs) in freshly-isolated CD150+CD34-c-Kit+Sca-1+Lineage- HSCs and evaluated their activity in 16-week transplantation assays. These experiments were performed with sgRNAs delivered through ribonucleoprotein (RNP) electroporation or with lentiviral transduction. Similar to our validation experiments, donor chimerism was again lower in the PB than in the immature KSL HSPCs from the Tada2b- and Taf5l-KO HSCs (Supplementary Fig. S2g–j). In the RNP KO transplantation assay, hematopoiesis was almost entirely donor-derived, we also observed significant reductions in white blood cell (WBC) counts in the Tada2b-KO and Taf5l-KO recipients (Supplementary Fig. S2k). Red blood cell (RBC) counts and platelet counts were not significantly reduced (Supplementary Fig. S2l–m). Together, these results confirmed SAGA complex members Tada2b and Taf5l as functional HSPC regulators that are required for normal hematopoiesis.

We next investigated the phenotype of Tada2b-KO and Taf5l-KO in our ex vivo HSPC cultures. Consistent with these knockouts displaying a block in differentiation, we observed an increase in the frequency of CD201+CD150+KSL population (described as the phenotypic (p)HSC population ex vivo that contains the functionally transplantable HSCs28,29) within the Tada2b-KO and Taf5l-KO cultures after 14-days (Fig. 3a). Numbers of pHSCs were not increased in these cultures however, suggesting this was due to a reduction in non-HSC populations rather than an increase in pHSC numbers (Supplementary Fig. S3a). We observed a loss of CD201-CD150-KSL and c-Kit+Sca-1+Lineage- progenitor populations (Supplementary Fig. S3b, c). This pHSC expansion phenotype was also seen when quantifying c-Kit+Lin-CD201+ cell frequencies (Supplementary Fig. S3d). Additionally, knockdown of TADA2B using previously reported short-hairpin RNAs (shRNAs)30 within ex vivo human HSPC cultures31 induced a similar increase in the frequency of immunophenotypic CD201+CD34+CD45RA-CD41- HSC compartment (Fig. 3b). These results provided further evidence that loss of SAGA complex components altered HSPC activity and that we could investigate this phenotype ex vivo.

HSPCs lacking SAGA complex members upregulate interferon pathways
Given these striking functional phenotypes from the genetic ablation of Tada2b and Taf5l, we were interested to better understand the consequences of these gene knockouts at the molecular level. We therefore performed single cell RNA-sequencing (scRNA-seq) of control, Tada2b-KO, and Taf5l-KO HSPC cultures at 14-days post-RNP electroporation. Consistent with the known heterogeneity of our HSPC cultures, initial clustering analysis identified four major cell types including a population of cells with an HSC signature and several progenitor cell types (Fig. 3c, Supplementary Fig. S3e, f). This analysis suggested loss of Tada2b or Taf5l increased the frequency of megakaryocyte progenitor cells (MkPs) within the HSPC cultures (Supplementary Fig. S3g), which we could validate by flow cytometry (Supplementary Fig. S3h).

To investigate the molecular consequences specifically within the HSCs, we specifically subclustered on this population (Fig. 3c). By combining this re-clustering with cell cycle analysis, we could identify sub-clusters resembling G1-phase HSCs, S-phase HSCs, and G2-phase HSCs (Fig. 3c). To avoid cell cycle-associated transcriptional changes, we focused on gene expression differences within the G1-HSC cluster. We observed major differences in the transcriptional programs expressed in the Tada2b- and Taf5l-KOs (Fig. 3d). Gene Ontology (GO) pathway analysis identified major changes in interferon (IFN) signaling (Fig. 3e, Supplementary Data 3-4). For example, interferon response genes Ifi44, Ddx60, and Oasl2, were significantly upregulated in the KO settings (Fig. 3f–h). Significant upregulation of IFN-related gene sets was also observed in other progenitor clusters (Supplementary Data 3-4). The similarity of these DEGs in the Tada2b and Taf5l KOs also implicate loss of SAGA complex activity as a common mechanism underlying these transcriptional changes. Corresponding with this gene signature, we observed protein level upregulation of known IFN response genes Sca-1 (Ly6e) and MHC-I (Supplementary Fig. S3i, j), although these did not reach statistical significance.

We were interested in whether similar gene expression changes were also induced in HSPCs in vivo. We therefore performed bulk RNA-seq on control, Tada2b-KO, and Taf5l-KO KSL HSPCs from the BM of our transplantation recipients. We also observed increases in IFN response genes in this context, suggesting this phenotype was not an artifact of the HSPC culture system but was tightly linked to the loss of these SAGA components (Supplementary Fig. S4a–c, Supplementary Data 5). However, within the in vivo setting, we could not observe changes in Sca-1 (nor MHC-I) by flow cytometry (Supplementary Fig. S4d, e).

To test whether SAGA complex members are not only necessary for restraining IFN signaling but sufficient for suppressing IFN genes, we overexpressed Tada2b in ex vivo expanded HSCs. We observed a decrease in CD201+CD150+KSL pHSC frequency in ex vivo HSPC cultures upon Tada2b overexpression (Fig. 3i), the opposite of the KO phenotype. Tada2b overexpression resulted in a suppression of several IFN-related genes in RNA-seq and was the top GO category in the set of significantly downregulated genes (Fig. 3j, k, Supplementary Fig. S4f, Supplementary Data 6), indicating that TADA2B expression plays an important role in regulating IFN gene expression in HSPCs.

To understand where the SAGA complex was influencing the intrinsic IFN pathway, we initially assessed inhibition of the upstream IFN pathway regulator TBK1. Inhibition led to ~50% reduction in the surface expression of the interferon-stimulated gene (ISG) Sca-1 (Fig. 4a). Similar effects were seen in Tada2b-KO and control backgrounds, suggesting that TBK1 activity was not unique to the Tada2b-KO setting. We next assessed whether IFN secretion (and signaling) could be contributing to the SAGA complex KO phenotype by performing an IFN secretion assay. We detected higher IFN levels in Tada2b-KO cultures as compared with control cultures (Fig. 4b).

To determine the contribution of secreted IFN (alpha/beta) on the SAGA complex KO-associated gene expression changes, we knocked out Tada2b in Interferon alpha/beta receptor alpha chain (Ifnar1)-deficient HSCs and performed bulk RNA-seq. The upregulation of IFN response genes was lost when Tada2b was knocked out in the Ifnar1-KO background (Fig. 4c–e), implicating IFNs as mediators of this gene expression pattern. However, another set of genes were similarly upregulated following loss of Tada2b in both the wild type (WT) and Ifnar1-KO backgrounds (Fig. 4f, g). The increased CD201+CD150+KSL pHSC frequency phenotype was also retained in the Ifnar1-deficient background (Supplementary Fig. S4g, h). Additionally, the Ifnar1-KO background did not rescue the Tada2b-KO differentiation defect in vivo (Supplementary Fig. S4i). These results implicate IFN signaling as one of several molecular pathways dysregulated by the SAGA complex in HSPCs, in line with the multifaceted functions of the SAGA complex11.

Altered SAGA complex chromatin activity associated with loss of adapter proteins
Loss of SAGA complex acetyltransferase enzymes (KAT2A and KAT2B) was recently shown to upregulate IFN signaling in the context of intestinal stem cells32. The study implicated mitochondrial dysregulation as a key driver of IFN secretion. We therefore quantified mitochondrial activity in Tada2b-KO HSPCs. We observed a modest but significant reduction in MitoTracker Orange CMTMRos (Fig. 5a), without a reduction in mitochondrial mass (quantified by MitoTracker Green FM) (Supplementary Fig. S5a). Within the HSPC cultures, reduction in mitochondrial activity was slightly more pronounced in the pHSC fraction compared to total Lin- cell population (Supplementary Fig. S5b). Consistent with reduced mitochondrial potential, Tada2b-KO HSPCs also displayed enhanced sensitivity to the mitochondrial Complex I inhibitor IACS-010759 (Fig. 5b).

To better understand the chromatin-level dysregulation caused by the loss of SAGA complex members in HSPCs, we next investigated the epigenetic marks mediated by the SAGA complex, histone H3 lysine 9 acetylation (H3K9ac) and histone 2B lysine 120 monoubiquitation (H2Bub). Loss of TADA2B, TAF5L, or TADA1 have recently been reported to destabilize the HAT domain in cell line models33. In line with this, we observed a ~20% loss of H3K9ac levels within Tada2b-KO HSPCs by intracellular flow cytometry (Fig. 5c), with similar results also seen in Taf5l- and Tada1-KO HSPCs (Supplementary Fig. S5c). By contrast, no significant change in H2Bub was observed in these KO HSPCs (Fig. 5d, Supplementary Fig. S5d). In line with a global loss of H3K9ac, we saw an overall reduction in chromatin accessibility in Tada2b-KO pHSCs by ATAC-seq (Fig. 5e, Supplementary Fig. S5e, Supplementary Data 7). Within the modest number of gained differentially accessible regions, we detected enrichment for IRF motifs (Fig. 5f) and interferon gene sets (Fig. 5g), which matches with the IFN expression pattern in our RNA-seq analysis.

To further probe the genome-specific changes in H3K9ac and H2Bub enrichment, we performed Chipmentation34 in Tada2b-KO and WT pHSCs. Interestingly, we observed a significant shift in H2Bub enrichment (Fig. 5h, Supplementary Data 7), with a gain in H2Bub at genes associated with STAT and IRF4 motifs (Fig. 5i). These genes were enriched in pathways associated with interferon signaling and cholesterol metabolism (Fig. 5j), while H2Bub was lost at genes associated with cytokine signaling (Supplementary Fig. S5f). Despite the global loss of H3K9ac, we did not observe statistically significant changes in H3K9ac enrichment (Supplementary Fig. S5g), although we could observe reductions in the level of H3K9ac enrichment at several gene loci (e.g., Kit and Stat5a) (Supplementary Fig. S5j, k). Incomplete loss of H3K9ac peaks may be due to the incomplete KO of Tada2b in our HSPC cultures. Nonetheless, these results provide important insights into the chromatin-level consequence of loss of SAGA complex members in HSPCs.

To investigate whether Tada2b-KO induced loss of SAGA complex HAT activity was likely to be the major consequence, we tested the effect of GSK699, a KAT2A/B inhibitor/degrader33. Similar to Tada2b-KO HSPCs, GSK699 induced a reduction in H3K9ac levels relative to dimethyl sulfoxide (DMSO) control (Fig. 5k). No changes to H2Bub levels were observed (Fig. 5l). GSK699 also induced an upregulation of Sca-1 (Fig. 5m) and increased immunophenotypic HSCs and MkPs (Fig. 5n, o), in line with the Tada2b-KO. Bulk RNA-seq of c-Kit+Sca-1+Lin- HSPCs also confirmed a correlation in transcriptional changes induced by treatment with GSK699 and the loss of Tada2b (Fig. 5p, q, Supplementary Fig. S5l, Supplementary Data 8). The overlapping molecular phenotypes of GSK699 and Tada2b-KOs suggest that TADA2B plays an important role in stabilizing the SAGA complex’s HAT module in HSPCs.

Loss of SAGA boosts engraftment and interferon signaling in a human MDS model
The dramatic block in hematopoiesis was reminiscent of the failure seen in hematological diseases such as MDS. Additionally, MDS samples are associated with IFN upregulation (Supplementary Fig. S6a) and mitochondrial dysfunction35. We therefore set out to evaluate whether loss of SAGA complex influenced the human MDS-L cell line model36. We knocked out SAGA complex members TADA2B, TAF5L, TADA1, or the AAVS1 control locus in MDS-L cells using CRISPR (Supplementary Fig. S6b). We then performed a competitive transplantation assay into immunodeficient mice expressing human IL-3 and GM-CSF (Fig. 6a). We observed a striking competitive advantage by the SAGA component KO MDS-L cells as compared with the control cells, with significantly higher chimerism in both the BM and spleen (Fig. 6b, c, Supplementary Fig. S6c). Additionally, we observed larger spleen weights in TADA1- and TAF5L-KO recipients (Supplementary Fig. S6d). However, there were no overt signs of leukemic transformation (Supplementary Fig. S6e) or recipient mortality.

To see if the same transcriptomic signature were present in the human SAGA complex KOs as in the mouse setting, we performed RNA-seq on the 12-week BM engrafted human SAGA complex KOs MDS-L cells as well as controls. We again observed a robust upregulation of IFN response genes upon SAGA complex member KOs (Fig. 6d, e, Supplementary Data 9). In addition to these IFN genes, we also observed an upregulation of myeloid genes sets including CSF3R and CD33 (Fig. 6e, f). Together, these results suggest the SAGA complex has various regulatory activities that are conserved between mouse and human.

Methods
Mice
Mice were housed in a 12h dark-light cycle, ambient temperature (20–22°C), and humidity-controlled (30–70%) environment with ventilated caging, sterile bedding, and unrestricted access to sterile food and water in the animal facilities at Stanford University or at the University of Oxford. C57BL/6-Rosa26CAG-Cas9 (B6J.129(Cg)-Gt(ROSA)26Sortm1.1(CAG-cas9*,-EGFP)Fezh/J; 026179) and C57BL/6-CD45.1 (PepboyJ; 002014) were purchased from The Jackson Laboratory. C57BL/6-CD45.2 mice purchased from The Jackson Laboratory (000664) or bred at the University of Oxford. C57BL/6-CD45.1/CD45.2 F1 mice were bred from C57BL/6-CD45.1 and C57BL/6-CD45.2 at Stanford University or the University of Oxford. NOD.Cg-Prkdcscid Il2rgtm1Sug Tg(SRa-IL3, CSF2)7-2/Jic (NOG-EXL) mice expressing human IL-3 and GM-CSF (NOG/IL-3/GM-CSF) were purchased from In-Vivo Science Inc. Infar1-KO (B6.bkg-Ifnar1tm1Agt/Mmjax) mice were provided by Caetano Reis e Sousa. All mice were 8–12 weeks at the experiment start point.

Ex vivo mouse HSC cultures
Mouse HSC cultures were initiated from c-Kit+ bone marrow38 from C57BL/6-Rosa26CAG-Cas9 mice. Following CO euthanasia, tibia, femur, pelvis, and vertebrae were collected and crushed to release bone marrow cells. Bone marrow was then stained with anti-cKit-APC antibody (clone 2B8, Biolegend), then incubated with anti-APC microbeads (Miltenyi) and c-Kit+ cells enrichment was performed using LS MACS column (Miltenyi) according to the manufacturer’s protocol. Enriched c-Kit+ bone marrow cells were cultured in HSC expansion media at 5% CO and 5% O in a CellBind coated cell culture plate (Corning, cat. CLS3337) with complete media changes every 2-3 days. HSC expansion media composition24,39: Ham’s F12 media supplemented with 1X penicillin-streptamycin-glutamine (PSG; Gibco), 1X HEPES, 1X insulin-transferrin-selenium-ethanolamide, 1mg/ml polyvinyl alcohol (Sigma), 100ng/ml recombinant mouse thrombopoietin (Peprotech), and 10ng/ml recombinant mouse stem cell factor (Peprotech). Detailed information on all products used are listed in the Supplementary Table 1. For validation experiments, HSC cultures were initiated with c-Kit+ bone marrow from C57BL/6-CD45.1, C57BL/6-CD45.2, or Ifnar1-KO mice, as indicated in the text. Where indicated, HSC cultures were supplemented with the TBK1 inhibitor GSK-8612 at 5μM or DMSO. Where indicated, HSPCs were cultured for 100nM GSK699, 10nM IACS-010759 or equivalent volume of DMSO.

Ex vivo and in vivo CRISPR screen
After 3-week ex vivo expansion of c-Kit+ bone marrow HSPCs, ~50 million cells were transduced overnight with lentivirus generated from one of 10 Bassik mouse CRISPR knockout sub-libraries40 (a kind gift from the Bassik lab; Addgene 1000000121-1000000130). Each sub-library contained ~20,000 elements. Lentivirus was generated using helper plasmids pCMV-VSV-G-RSV-Rev and pMDLg/pRRE. To reduce multiple integrations per cell, we used a transduction efficiency of 20–30%, to achieve 500–750× coverage (10–15 million transduced HSPCs per sub-library). Two days later, transduced HSPCs were selected for using puromycin for 72h. HSPCs were recovered and expanded in culture for another 9 days. For ex vivo timepoints, 20 million HSPCs were harvested for DNA extraction. At day-14 post-transduction, HSPCs were transplanted into lethally-irradiated (10 Gy) C57BL/6-CD45.1 recipients (1–2 million HSPCs per recipient; >10 million per sub-library across 6–8 mice). A total of 65 mice were transplanted for the genome-wide screen, while 24 mice were transplanted for the Gene Expression-focused screen. After 10–12-weeks, whole bone marrow and spleen were harvested. From the whole bone marrow, c-Kit+ Sca-1+ Lineage- or c-Kit+ Lineage- cells were isolated by FACS. The remaining bone marrow and spleen cells were pooled and CD45R+ B cells, CD4/CD8+ T cells, Mac1/Gr1+ myeloid cells, and Ter119+ erythroid cells were isolated via MACS columns.

Screen analysis
Genomic DNA was extracted for all screen populations separately according to the protocol included with QIAGEN Blood Mini or Midi Kit. Using known universal sequences present in the lentivirally incorporated DNA (oMCB 1562 and oMCB 1563), sgRNA sequences were amplified and prepared for sequencing by two sequential polymerase chain reactions as previously described40. Products were sequenced using an Illumina Nextseq to monitor library composition (20–40 million reads per sample). Trimmed sequences were aligned to libraries using Bowtie, with zero mismatches tolerated and all alignments from multi-mapped reads included. Guide composition and comparisons across each cellular fraction were analyzed using CasTLE version 1.040. Briefly, enrichment of individual sgRNAs was calculated as a median-normalized log ratio of the fraction of counts. For each gene, a maximum likelihood estimator was used to identify the most likely effect size and associated log-likelihood ratio (confidence score) by comparing the distribution of gene-targeting guides to a background of non-targeting and safe-targeting guides. P-values were then estimated by permuting gene-targeting guides, and hits were called using FDR thresholds calculated via the Benjamini-Hochberg procedure.

Ex vivo validation assays
To generate Taf5l or Tada2b-KO HSCs, 2×105 HSCs derived from two-week C57BL/6 c-Kit+ bone marrow (as described in the Ex vivo HSC culture section) were electroporated with 6μg recombinant HiFi Cas9 (IDT) pre-complexed with 3.2μg synthetic sgRNA (Synthego; sequences listed in the Supplementary Table 1) using a Lonza 4D-Nucleofector using program EO100 in complete primary P3 solution (16.4μl of P3 primary cells solution + 3.6μl supplement per reaction) and then returned to HSC expansion media41. Electroporation of Cas9 only or Rosa26-targeting RNP were used as controls. At indicated timepoints, cells were collected for KO frequencies or flow cytometry analysis. To determine KO frequencies, gDNA was isolated via QuikExtract for PCR (primer sequences listed in the Supplementary Table 1) and Sanger sequencing with KO frequencies determined using ICE software (Synthego). Alternatively, lentivirus carrying Tada2b-2A-GFP cDNA, or GFP cDNA were generated and transduced into 3-week old Cas9-expressing (or wild-type) C57BL/6 HSC cultures, as described in the Ex vivo and in vivo CRISPR screen section. Flow cytometric analysis was then performed following antibody staining (CD201-APC, CD150-PE/Cy7 cKit-BV421, Sca-1-PE, Gr1-FITC, Ter119-FITC, CD4-FITC, CD8-FITC, CD45R-FITC, CD127-FITC, all diluted 1:500 in PBS) for 30 min at 4°C, then washed and analyzed using an LSR Fortessa (BD) with propidium iodide (Biolegend) as a live/dead cell stain. All flow cytometric data analysis was performed using FlowJo and statistical analysis performed using Prism.

Flow cytometric analysis
For HSPC culture flow cytometric analysis, cells were antibody stained (CD201-APC, CD150-PE/Cy7, cKit-BV421, Sca-1-PE, Gr1-APC/eFluor780, Ter119-APC/eFluor780, CD4-APC/eFluor780, CD8-APC/eFluor780, CD45R-APC/eFluor780, CD127-APC/eFluor780, all diluted 1:500 in PBS, staining volume 100ul per sample) for 30 min at 4°C, washed and then analyzed using a LSR Fortessa (BD) with propidium iodide (Biolegend) as a live/dead cell stain. Peripheral blood samples were analyzed using a Horiba Micros ESV60 complete blood counter or by flow cytometry. For flow cytometry, peripheral blood leukocytes were stained with antibodies (CD45.1-PE/Cy7 1:400, CD45.2-BV421 1:400, Mac1-PE 1:2000, Gr1-PE 1:2000, CD45R-APC/Cy7 1:1000, CD4-APC 1:2000, CD8-APC 1:2000 in PBS, staining in 100ul volume) following red blood cell lysis. Samples were then run on an FACSAria II or LSR Fortessa using propidium iodide as a live/dead cell stain, with CD45.1+ cells sorted for KO analysis. Data analysis was performed using FlowJo. Bone marrow chimerism was also analyzed by sequential antibody staining with a lineage cocktail (Gr1-biotin, Ter119-biotin, CD4-biotin, CD8-biotin, CD45R-biotin, CD127-biotin, all 1:33 in PBS, staining volume 100ul per sample) followed by Streptavidin-APC/eFluor780 1:100, CD34-FITC 1:25, c-Kit-APC 1:100, Sca-1-PE 1:100, CD45.1-PE/Cy7 1:33, CD45.2-BV421 1:33, CD16/32-BV711 1:100, MHCI-AF700 1:300 OR Streptavidin-APC/eFluor780 1:100, CD34-FITC 1:25, c-Kit-APC 1:100, Sca-1-PE 1:100, CD45.1-PE/Cy7 1:33, CD45.2-BUV395 1:33, CD48-BV421 1:100, CD150-BV785 1:100 in PBS, staining volume 100ul per sample). Samples were then run on an FACSAria II or LSR Fortessa using propidium iodide as a live/dead cell stain, with CD45.1+c-Kit+Lineage- cells sorted for KO analysis. For intracellular staining, cells were fixed and permeabilized using the BD Transcription Factor Buffer set and stained with an anti-Ubiquityl-Histone H2B (Lys120)-PE or anti-Acetyl-Histone H3 (Lys9)-PE antibodies for 45 min at 4°C (both diluted 1:100 in the Perm/Wash buffer from the kit, staining in 100μl per sample), then washed and analyzed. For the assessment of mitochondrial mass and mitochondrial activity, cells were stained with MitoTracker Green FM (1:1000 in PBS from stock prepared according to the manufacturer’s instructions, staining in 100μl volume) or MitoTracker Orange CMTMRos (final concentration 30nM in PBS) dyes, respectively for 45 min at 37°C according to the manufacturer’s recommendations, then washed and analyzed. All flow cytometric data analysis was performed using FlowJo and statistical analysis performed using Prism. Antibody information is listed in the Supplementary Table 1. Immunophenotypic definitions of the cell populations described in this manuscript are listed in Supplementary Table 2 below and Supplementary Fig. S7a–d.

Western blotting
Protein lysates extracted from control and KO HSCs were run on a 4–12% Bis-Tris gel and blotted using antibodies against TADA2B (St. John’s Laboratory, STJ194361-200), TAF5L (Proteintech, 19274-AP), TADA1 (Proteintech, 20337-1-AP), GAPDH (Bethyl Laboratories, A300-641A), or histone H3 (Abcam, ab1791). Following secondary staining with peroxidase-conjugated goat anti-rabbit IgG (Sigma Aldrich, A6667), blots were imaged using a ChemiBlot Imager and the Precision Plus Protein Kaleidoscope kit (Biorad 1610375).

Transplantation assays using RNP-KO HSCs
CD150+CD34-Kit+Sca-1+Lineage- HSCs were isolated using a FACSAria II (BD) from C57BL/6-CD45.1 bone marrow and electroporated with either Cas9/sgRNA targeting Tada2b, Taf5l, or Rosa26 (same protocol as Ex vivo validation assays section) and then expanded for 7-days before transplantation into lethally-irradiated (10 Gy) C57BL/6-CD45.2 recipient mice (500 HSC equivalent per recipient) alongside 1 million whole bone marrow competitor cells isolated from C57BL/6-CD45.1/CD45.2 F1 mice. At the endpoint, bone marrow and spleen were collected and fixed (4% paraformaldehyde for 48h) for hematoxylin and eosin staining (performed by the Stanford Animal Histology Service).

Transplantation assays using RNP-KO HSPCs
C57BL/6-CD45.1 c-Kit+ bone marrow from were expanded for 14-days and then electroporated with either Cas9/sgRNA targeting Tada2b, Taf5l, Tada1, Runx1 or Rosa26 (same protocol as Ex vivo validation assays section). After a second 14-day culture, cells were transplanted into lethally-irradiated C57BL/6-CD45.2 recipient mice (5000 CD201+CD150+c-Kit+Sca-1+Lin- cells per recipient) alongside 1 million whole bone marrow competitor cells isolated from C57BL/6-CD45.1/CD45.2 F1 mice. Peripheral blood (PB) analysis was performed as described in the Flow cytometric analysis section. BM and spleens were harvested at the endpoint of the experiment. For secondary transplantation assays, 5 million whole bone marrow cells from primary recipients were transplanted into lethally-irradiated recipient mice and analyzed as above.

Interferon secretion assay
NIH/3T3 cells were incubated with HSC culture supernatant (48h after the last media change) or with recombinant IFNa (dilution series from 100ng/ml to 10pg/ml; Biolegend, 752802) for 3.5h and then collected for RNA extraction using the Quick-RNA microprep kit (Zymo Research, R1055). Following cDNA synthesis using the SSIV Reverse Transcriptase (ThermoFisher, 18090050), Taqman qPCR was run to quantify Ifit1 (ThermoFisher, Mm00515153_m1) and Gapdh (ThermoFisher, Mm99999915_g1) expression.

Human shRNA knockdown assay
Four-day cultured human umbilical cord blood CD34+ HSPCs (originally purchased from NHSBT) were transduced with lentivirus carrying control or TADA2B targeting shRNAs (reported previously30) and were then cultured for 14 days. HSPCs were cultured in 3A media conditions31: IMDM supplemented with Butzyamide (Cellaid), 740Y-P (MedChemExpress), UM729 (StemCell Technologies), and FLT3L (Peprotech). HSPCs were then stained with CD34-APC/Cy7, CD41-APC, CD45RA-BV785, and CD201-PE and analyzed by flow cytometry using PI as a live/dead cell stain.

Human MDS-L studies
Human mScarlet-expressing MDS-L cells36 (a kind gift from Tohyama laboratory, Kawasaki Medical School) were generated previously42 and maintained in RMP1640 supplemented with 10% fetal bovine serum, 1X PSG, and 10ng/ml human IL-3 (Peprotech). MDS-L/mScarlet/Cas9 cells were initially generated using pLentiCas9-Blast lentivirus (Addgene 52962). MDS-L/mScarlet/Cas9 cells were then transduced and Puromycin-selected with indicated pMCB306 lentivirus expressing sgRNA and GFP (a kind gift from the Bassik lab; Addgene 89360). After confirming high KO efficiency, a 1:1 mixture of MDS-L/Cas9 cells and MDS-L/mScarlet/Cas9/sgRNA cells were then transplanted into NOG mice expressing human IL-3 and GM-CSF. After 12-weeks, bone marrow and spleen were harvested from recipient mice for flow cytometric analysis.

Bulk RNA-seq
RNA was extracted from indicated cell populations using the Zymo Direct-zol RNA MicroPrep kit and RNA sequencing was performed by Nocogene using polyA enrichment and sequencing on a Novoseq X (20M reads/sample). Reads were mapped to the mouse GRCm38 (mm10) reference genome using STAR (v.2.5.1b). Raw read counts were generated with STAR using the GeneCounts function. Differential expression in RNA-seq was analyzed using the R package DESeq2 (Love et al. 2014).

ATAC-seq
HSCs were pelleted at 400×g and 4°C. Next, pellets were gently resuspended in ice-cold 50μl of lysis buffer (10mM Tris-HCl pH 7.4, 10mM NaCl, 3mM MgCl , 0.1% IGEPAL CA-630) and spun down at 500×g for 10min and 4°C. The supernatants were discarded and pellets gently resuspended in 50μl of transposition reaction mix (25μl of tagment DNA buffer (Nextera, Illumina), 2.5μl of tagment DNA enzyme (Nextera, Illumina), 22.5μl of nuclease free water) and incubated at 37°C for 30min. Tagmented DNA was purified using MinElute PCR purification kit (Qiagen) and size selected for 70–500 base pairs (bp) using Ampure XP beads (Beckman Coulter). Libraries were constructed and amplified using 1.25μM Nextera index primers and NEBNext High-Fidelity 2× PCR Master Mix (New England BioLabs). A quantitative PCR was run to determine the optimal number of cycles. Libraries were gel size selected for 165–300 bp fragments. Sequencing was carried out using an Illumina NexSeq 550, paired-end, 2×100bp. Reads were mapped to the mm10 genome using Bowtie2, and SAM files were converted to BAM format with Samtools. The BAM files were sorted and indexed using Samtools, and mapping statistics were generated. PCR duplicates were removed with Samtools, and BED files were created using bedtools bamtobed. Reads mapping to chrM and regions in the ENCODE mm10 blacklist were removed from the BED files. Genome coverage was calculated using bedtools genomecov to produce bedGraph files. A scale factor was calculated by dividing 5,000,000 by the number of regions in each BED file, and applied to the bedGraph files. BigWig files were generated using bedGraphToBigWig. Peaks were called with MACS2 in broad mode. Differential peak analysis was performed using DiffBind, and differential peaks were annotated and analyzed for motif enrichment using HOMER.

Chipmentation
CD201+CD150+KSL cells were fixed with 1% formaldehyde (10 minutes incubation at room temperature with rotation), then washed in PBS. Cell pellets were then resuspended in 120µl of lysis buffer (50mM Tris pH 8, 10mM EDTA, 0.5% SDS, 1.2µl PIC per every 120µl in ddH O), and sonicated in Covaris AFA fiber microtubes (Covaris, cat. 520045) aiming for 200bp fragments. SDS was neutralized with 1% Triton (25µl of 10% Triton X100 for every 225µl of sample), and samples were incubated at room temperature for 10min with rotation. 5µl of fresh Protein A dynabeads (ThermoFisher, cat. 10001D, washed with PBS to remove the residual storage buffer), were added per reaction, followed by incubation at 4°C for 30min with rotation. Samples were then placed on a magnet and cleared chromatin was collected, 5% of each reaction was allocated to a new DNA LoBind tube and stored at −20°C as input control. Protein A dynabeads (ThermoFisher 10001D, 10µl per antibody per sample), were washed twice with the wash buffer (200µL PBS with 0.5% BSA + 1µl PIC per antibody per sample), and resuspended in 200µl wash buffer per reaction. Beads were then transferred to fresh DNA LoBind tubes and 1µl of antibody was added to each reaction. The beads were then incubated for 3–4h at 4°C with rotation. The beads were then washed twice in a new wash buffer (500µl PBS + 0.5% FCS + 1µl Tween, 250µl for each wash), 250µl of cleared chromatin was added to each antibody-bead tube followed by an overnight incubation at 4°C with rotation. The following day each reaction was washed three times with 150µl of RIPA buffer (50mM HEPES pH 7.6, 50mM LiCl, 1mM EDTA, 1% NP-40, 0.7% Na Deoxycholate in ddH O), once with 150µl of TE buffer, and once with 150µl of 10mM Tris pH 8. The beads were then re-suspended in 25µl of tagmentation buffer (prepare 5X stock of 50mM Tris pH8, 25mM MgCl and 50% dimethylformamide in ddH 0, and dilute to 1X with ddH 0) pre-warmed to 37°C, followed by addition of 5µl of diluted Tn5 transposase (Illumina, cat. 20034197, 1µl of Tn5 per 4µl of tagmentation buffer) and incubation at 37°C for 5min with rigorous mixing. The beads were then immediately washed once with the RIPA buffer, once with 10mM Tris pH 8 (150µl per tube) and resuspended in ddH O (22.5µl per tube). For input controls, 2.5µl 50mM MgCl2 was added to 12.5µl of input chromatin sample, followed by the addition of 6µl of 5× tagmentation buffer, 1µl of Tn5 and 8µl of ddH O; samples were then incubated at 37°C for 10min (mixed after 5min), placed on ice and 20µl were taken for library preparation step. For library preparation, the beads were supplemented with 25µl of 2X NEBNext Ultra II Q5 Mastermix (New England Biolabs, cat. M0544S), 1.25µl of universal adapter primer and 1.25µl of index adapter primer and subjected to the following PCR programme: 72°C 5min, 95°C 5min, 12 cycles of (98°C 10s, 63°C 30s, 72°C 3min), 72°C 5min, 12°C indefinite hold. Upon library preparation PCR, the tubes were transferred to the magnet and supernatant was put in fresh DNA LoBind tubes. 50µl of the AMPure XP beads (Beckman Coulter, cat. A63881) were added to each tube, and the samples were incubated at room temperature for 2min. The beads were then washed twice with 150µl of freshly prepared 80% ethanol, incubated at room temperature for 2min, ethanol was removed and the beads were air-dried, resuspended in 11µl ddH O and incubated at room temperature for 2min again. The tubes were put on the magnet and 10µl were collected from each tube. QC and quantification of the samples were performed using D1000 high sensitivity ScreenTape (Agilent, cat. 5067-5584) and KAPA Library Quantification Kit (Roche, cat. KK4824). Library amplification was performed using KAPA HiFi HotStart Library Amp Kit (Roche, cat. KK2612) according to manufacturer’s recommendations.

Raw reads were assessed for quality using FASTQC and adapters were trimmed with Trimmomatic. Reads were mapped to the mm10 genome using Bowtie2, and SAM files were converted to BAM format with Samtools. The BAM files were sorted and indexed using Samtools, and mapping statistics were generated. PCR duplicates were removed with Samtools, and BED files were created using bedtools bamtobed. Reads mapping to chrM and regions in the ENCODE mm10 blacklist were removed from the BED files. Genome coverage was calculated using bedtools genomecov to produce bedGraph files. A scale factor was calculated by dividing 5,000,000 by the number of regions in each BED file, and applied to the bedGraph files. BigWig files were generated using bedGraphToBigWig. Peaks were called with MACS2 in both broad and narrow modes. Differential peak analysis was performed using DiffBind, and differential peaks were annotated and analyzed for motif enrichment using HOMER.

Single-cell RNA-seq analysis
At day-14 post-electroporation, control, Tada2b-KO, or Taf5l-KO HSC cultures were collected for RNA-seq analysis using a Gene Expression kit v3 (10× Genomics) according to the manufacturer’s protocol with libraries sequenced on a NovaSeq 6000 S4 (Illumina). Raw gene counts were obtained by aligning reads to the mouse genome using CellRanger software (v.4.0.0) (10× Genomics). For quality control, cells with unique feature counts over 2500 or less than 200 were removed as well as cells that have >5% mitochondrial counts. Ambient cell free mRNA contamination was removed using SoupX for each individual sample. Multiplets were filtered out using DoubletFinder. The core statistical parameters of DoubletFinder (nExpand pK) used to build artificial doublets for true doublet classification were determined automatically using recommended settings. The SCTransform-based integration workflow of Seurat was used to align data, using default settings. In brief, the integration workflow searches for common gene modules (anchors) in cells with similar transcriptomes. Individual samples after undergoing quality control are integrated in a stepwise fashion, using cellular sequencing depth as a covariate to mitigate technical artifacts. After combining the samples into a single dataset or Seurat object, genes were projected into principal component space using the principal component analysis (RunPCA). The first 100 dimensions were used as inputs into the FindNeighbours, FindClusters and RunUMAP functions of Seurat. In brief, a shared-nearest-neighbor graph was constructed based on the Euclidean distance metric in principal component space, and cells were clustered using the Louvain method. RunUMAP functions with default settings were used to calculate 2D UMAP coordinates and search for distinct cell populations. Distinct cell populations were annotated based on conical marker genes. Monocle3 (v.0.2.1.) was used to generate the pseudotime trajectory analysis of HSCs. Cells were re-clustered as described in and used as input into Monocle to infer cluster and lineage relationships within a given cell type. Specifically, UMAP embeddings and cell sub-clusters generated from Seurat were converted to a cell_data_set object using SeuratWrappers (v.0.2.0) and then used as input to perform trajectory graph learning and pseudotime measurement through reversed graph embedding with Monocle. Cell cycle analysis was performed in Seurat using a list of cell cycle markers from Tirosh et al.43. Differential gene expression was performed in Seurat using the MAST package under default conditions.

Ethics
All animal experiments were approved by the Administrative Panel on Laboratory Animal Care at Stanford University, the UK Home Office, or the Animal Care and Use Committee of the Institute of Medical Science University of Tokyo. Human primary cell experiments were approved by the University of Oxford’s research ethics committee (OxTREC-574-23).

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

Data availability
Data supporting the findings of this work are available within the paper and its Supplementary Raw datasets and materials generated and analyzed during the current study are available via NCBI’s Gene Expression Omnibus (GEO) under accession numbers GSE312143 (CRISPR screen), GSE312125 (scRNA-seq), GSE312282 (bulk RNA-seq), GSE312290 (ATAC-seq), GSE312126 (ChIP-seq). The genome-wide CRISPR screen data are available via an interactive web application: www.hematopoiesiscrisprscreens.com. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Haney and colleagues present a genome-wide in vivo CRISPR-Cas9 knockout screen in mouse “hematopoietic stem cells”, which identifies structural components of the SAGA complex, particularly Tada2b and Taf5l, as regulators of hematopoiesis. The authors showed ex vivo and in vivo that knockout of these genes results in a block in differentiation from early stem/progenitor cells. Molecular profiling reveals upregulation of interferon-stimulated genes following knockout in the hematopoietic stem/progenitor cell compartment, and loss of these genes enhances outgrowth in a myelodysplastic syndrome model.\n\nThe approach presented in this study could be enormously enabling for the field of hematopoietic research, given that the scale of screens that can be conducted using the ex vivo methods described here, could enable many further screens. Moreover, the phenotypes observed are of potential interest. However, there are some limitations to the work as presented. Specifically, the hits from the screen appear insufficiently validated, there is ambiguous interpretation of the phenotypes, and a lack of significant mechanistic insight based on these findings. I detail these specific concerns below.\n\nMajor Concerns:\n1. The robustness of the screen is unclear. HSPCs were transduced with the library with limited coverage and subjected to extensive ex vivo expansion prior to transplantation, during which functional changes by knockout of critical factors could introduce significant bias. Although controlled subsequently, this likely impacts the power of this screen. Positive controls (e.g. known stem cell regulators) should be evaluated and discussed to confirm the validity of the screen. The authors also compared HSPCs versus individual lineages, are the well-established TFs for lineage commitment enriched in the screen?\n2. The term “HSCs” is inconsistently applied to both ex vivo cultured HSPCs and phenotypic HSCs. More broadly, the manuscript is ambiguous about the specific stages of differentiation impacted by knockout of the targets in validation experiments. This needs to be more clearly discussed and delineated throughout.\n3. The SAGA complex contains multiple modules. Although prior studies on other subunits are briefly summarized in the introduction, it is perplexing to see that the other components, particularly the catalytic subunits, were not included in the focused CRISPR screen or experimentally explored. Without addressing these, claims about SAGA complex function remain overstated.\n4. Related to the previous point, given the well-established role of the SAGA complex in transcriptional regulation, the impact of knockouts on chromatin biology and gene regulation relevant to hematopoiesis should be explored. At a minimum, given the extensive literature in this field, how gene expression changes observed in this setting relate to other perturbations involved in gene regulation that impact HSCs should be examined. Some of this can be achieved by examining publicly available datasets.\n5. (sc)RNA-seq results showed upregulation of IFN genes, but the causal relationship between this signature and the observed phenotype as well as the unchanged key markers in vivo remain largely underdeveloped. This requires further development, as discussed in point 4 above.\n6. The use of an MDS cell line appears a little arbitrary as there is no evidence presented that the candidate genes are mutated, dysregulated, or otherwise implicated in MDS. Can better rationale for this exploration be presented?\n\nMinor Concerns:\n1. Figures need revision-1c, 3d, and 4f lack proper labeling, 1e uses incorrect nomenclature for mouse genes, 2g used inconsistent color schemes.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "In this manuscript Haney et al describe an in vivo CRISPR/Cas9 screen to evaluate genes required for normal hematopoietic differentiation. They develop an ex vivo culture system to maintain HSCs allowing expansion and lentiviral transduction, followed by transplantation into lethally irradiated recipient mice and an in vivo readout. Using this elegant system they identify components of the SAGA complex as mediators of hematopoietic differentiation. They show that loss of these components leads to upregulation of the interferon response mouse hematopoietic cells. Finally, they show that knockout of these genes in the MDS-L model promotes engraftment and growth of these cells in a xenograft model. The in vivo screening system is exciting, and the role of SAGA in hematopoiesis is intriguing. Overall, the paper is clearly written, although lacking somewhat in discussion, but there is a lack of clarity as to what SAGA’s role in hematopoietic differentiation is. I lay out these concerns plus some additional points below:\n-The details of the whole-genome screen are not clearly described. Was each sub-pool of the library used to infect 50 million HSCs and then transplanted into 65 recipients? Or is the 50 million cells and 65 mice the total for the entire screen (all the sub pools combined)? Was the screen done in singlicate or replicate? Having specific numbers and a clearer description is helpful in assessing the representation and potential coverage of the screen. The schematic in Figure 1a also does not contain much detail or add much in explaining how the screen was performed. One of the more novel and exciting aspects of this report is the whole-genome in vivo screening approach, so additional details are important.\n-1-2 million HSCs were transplanted per mouse, however generally it is thought that many fewer HSC’s actually contribute to adult hematopoiesis (maybe around 100,000), so it’s unclear how this would impact representation within the screen. Did the authors evaluate sgRNA representation in the pooled HSC’s prior to transplantation, or in either the HSC or mature cell pools? If possible, some measure of this should be included in the supplemental methods/data.\n-“Here we refer to the cells in these cultures as HSCs; we acknowledge that these cultures include hematopoietic progenitor cells but for simplicity we will refer to these as HSC cultures.” Why not refer to these as hematopoietic stem and progenitor cells (HSPCs), which would be more accurate, and the abbreviation is just as easy to use throughout the paper? I feel that this term is more accurate and will avoid potential confusion.\n-Were these the only 4 members of the SAGA complex to score in the screens? What about other members of the complex or the catalytic subunit, KAT2A?\n-It’s a little unclear what is being proposed as a model for the function of SAGA in hematopoiesis. There are potentially multiple different SAGA complexes or modules with different component members. Is the hypothesis that the components of SAGA complex identified in this screen make up a particular complex important for directly regulating a subset of genes important for hematopoiesis? Are there published ChIPseq data for KAT2A or other SAGA complex members that might shed light on which of these effects is direct?\n-The focus here is on SAGA-mediated regulation of the interferon response. However, it is not at clear that this is mediating the hematopoietic phenotype. Is there a hematopoietic phenotype to the Ifnar1 knockout? Did any component of the interferon response score in the original genome-wide screen? What is mediating the hematopoietic differentiation defect?\n-What is the SAGA target that mediates the hematopoietic differentiation defect? Can the authors cross-reference the list of genes or pathways up- or down-regulated in the SAGA mutant cells with the genes that scored in the genome-wide screen? This might provide insight into the mechanistic role that SAGA plays in hematopoiesis.\n-The authors make the case that knockout of SAGA complex members supports the development of MDS/AML. To my knowledge, mutations in SAGA complex members have not been reported in clinical cohorts of MDS/AML. Are there any data to suggest that there is up- or down-regulation of these genes during disease formation, or anything else to suggest that changes in SAGA function participate in disease biology in humans?\n\nMinor issues:\n-Page 2, Line 65-67 (and throughout the manuscript): Mouse genes should have the first letter capitalized, and the remainder should be lower case letters and then italicized. Mouse protein names should have all their letters capitalized. So, “Kat2a” should be KAT2A. See https://www.informatics.jax.org/mgihome/nomen/short_gene.shtml for further reference.\n-Figure 1e-h: The gene names are capitalized here, but should be lower case.\n-Figure 3d: the legend says, “IFN response genes are highlighted in red.” However, no genes are highlighted in red in the figure.\n-Figure 4a: The figure suggests that there is a highly significant statistical difference between the “Control +TBKi” and “Tada2b KO +TBKi” conditions. However, by eye these two conditions look identical. Can the authors confirm whether this is correct or not?\n-Methods describing analysis of bulk RNAseq are not included in the methods section.", "reviewer_4": "Haney et al performed a genome-wide CRISPR KO screen in ex vivo expanded murine cKit+ cells and following transplantation identified gene edits corresponding to members of the SAGA complex that were enriched in HSPCs vs mature cells in vivo. The results were confirmed using a focused CRISPR screen targeting around 2000 genes. The authors focus on Tada2b and Taf5l KO and show an accumulation of ST-HSC/progenitors in bone marrow (but not LT-HSCs). Leveraging single cell transcriptomics in CD201+CD150+KSL (ex vivo cultures), the authors show activation of IFN genes in KO cells compared to controls. Over expression of Tada2b dampened these signatures. Increased IFN secretion from cultured KO Sca1+ cells, reduced numbers of Sca1+ cells following GSK-8612 and Tada2b edits in ifnar KO cells showed elevation of alternate sets of genes (that maintained elevation of HSCs) but not of IFN genes. Gene edits were then created in MDS-L cells and accumulation of these in xenograft BM/Spleen and activation of IFN gene sets in harvested cells.\nThis is an elegant manuscript with well-designed experiments and clear writing. The mouse experiments in particularly are very well done.\nFigure 1: Was this a single expansion culture and transplant of cells divided across 65 recipients with material pooled and sorted to identify sg enriched in stem/progenitors? If so, the schematic could be made clearer. How many recipient mice were used for the 2000 gene (SAGA enriched) screen?\nFigure 2: Please show the flow gating strategy used for immunophenotyping. Particularly for the BM LT-HSCs vs ST/LT-HSCs and pHSC/MPPs (Figure 2 c-f) given the difference in donor chimerism (LT-HSCs being similar PB WBCs).\nFigure 3:\n- Phenotypic KO LT-HSCs expanded ex vivo (Fig 3a/128-130) but KO LT-HSCs were lower in vivo? Showing total cell expansion numbers rather than frequencies and flow cytometry gating will be helpful (ditto 3i and S4g)\n- Comparisons were limited to HSCs in G1- was the IFN signature also increased in MPPs in G1?\n- MkP numbers appear to be increased in Tada2b and Taf5l KO?\nOverall, there is a lot of cell type nomenclature throughout this paper (MPP/HSC, pHSC, LTHSC, STHSC, Mkp etc) with very little description of how these are classified (e.g. immunophenotype). Please provide a table with cell type definitions or flow plots so the readers can interpret these results.\nFigure 5: Although conceptually MDS is attractive given expansion of progenitors and impaired differentiation but it is a big leap in the absence of somatic mutations/transcriptomic evidence that point to the SAGA complex in some way. The message needs to be toned down.\n- have you interrogated transcriptomic datasets (human MDS CD34+ vs healthy CD34+ or HR-MDS vs LR-MDS) for differences in SAGA complex gene expression?\n- MDS-L cells are an IL-3 dependent cell line generated from an MDS patient. I would not refer to these as MDS cancer cells/ mouse model of human MDS. Replace all labels to clearly state that these are MDS-L cells in main and supplemental figures. The data are data is consistent with expansion of KO MDS-L cells, but these cells don’t circulate/generate mature cells in vivo. Did the authors perform flow cytometry on retrieved BM-MDSL cells to interrogate their differentiation potential? What do you mean by upregulation of AML/MDS genes (Fig 4SI)?\nMinor-\n• Line 122. S2e-f is incorrect (S2d and f).\n• Figure 1h graph heading is covering the h.\n• Figure 3a legend says two guides were used (identified with grey and black dots- only grey dots present). Also says n=6, however plots show n=4 for control, n=7 for Tada2B and n=5 for Taf5I, please clarify.\n• Line 616 Figure 3d –Says IFN genes are highlighted in red, they are not. Please fix.\n• Line 619 Figure 3d- please fix labels to: Ifi44 (f), Oasl2 (g) and Ddx60 (h).\n• Line 620 Figure 3d “E.” not sure what this is referring to, is it a mistake?\n• Line 704 Figure S3 (g-h) fix label: please change to: Sca1 MF1 (g) and MHC-1 MFI (h).\n• Figure S3 (g-h) plots say ns. Is this correct? Looks like it should be significant. This is also discussed in text lines 157 and 158- implying this was significant observation.\n• Lines 174-175 and Figure 4a (lines 631-632) Both discuss changes in Sca1 MFI, however plot y-axis says “pHSCs frequency normalized to DSMO”?"}, "author_response": "We would like to thank all four Reviewers for their positive and constructive feedback. Our responses\nto each comment are detailed in blue font below.\nReviewer #1 (Remarks to the Author):\nIn this manuscript, Haney and colleagues present a genome-wide in vivo CRISPR-Cas9 knockout\nscreen in mouse “hematopoietic stem cells”, which identifies structural components of the SAGA\ncomplex, particularly Tada2b and Taf5l, as regulators of hematopoiesis. The authors showed ex vivo\nand in vivo that knockout of these genes results in a block in differentiation from early\nstem/progenitor cells. Molecular profiling reveals upregulation of interferon-stimulated genes\nfollowing knockout in the hematopoietic stem/progenitor cell compartment, and loss of these genes\nenhances outgrowth in a myelodysplastic syndrome model.\nThe approach presented in this study could be enormously enabling for the field of hematopoietic\nresearch, given that the scale of screens that can be conducted using the ex vivo methods described\nhere, could enable many further screens. Moreover, the phenotypes observed are of potential interest.\nHowever, there are some limitations to the work as presented. Specifically, the hits from the screen\nappear insufficiently validated, there is ambiguous interpretation of the phenotypes, and a lack of\nsignificant mechanistic insight based on these findings. I detail these specific concerns below.\nMajor Concerns:\n1. The robustness of the screen is unclear. HSPCs were transduced with the library with limited\ncoverage and subjected to extensive ex vivo expansion prior to transplantation, during which\nfunctional changes by knockout of critical factors could introduce significant bias. Although\ncontrolled subsequently, this likely impacts the power of this screen. Positive controls (e.g. known\nstem cell regulators) should be evaluated and discussed to confirm the validity of the screen. The\nauthors also compared HSPCs versus individual lineages, are the well-established TFs for lineage\ncommitment enriched in the screen?\nWe thank the Reviewer for this comment. We have added the following data to address concerns\nregarding screen robustness and interpretation:\n1. To address concerns of library coverage during ex vivo expansion: new Figure S1b indicates\nminimal sgRNA library loss compared to plasmid library following transduction (day 0) and\npre-transplantation at day 7.\n2. A positive control from the screen, Runx1, has now been experimentally validated and shows\nconcordance with screen results (new Figure S1m-q, new text lines 106-113).\n3. Individual lineages are now compared (new Figure S1j-l) highlighting established lineage\ncommitment genes (new text lines 102-104).\nThese newly included data suggest that these screens can identify well established regulators of\nhematopoiesis and lineage commitment as well as novel regulators such as the SAGA complex. We\nacknowledge the limitations of this screening approach that the reviewer has pointed out, in a new\nLimitations of Study section of the Discussion (new text lines 329-340).\n2. The term “HSCs” is inconsistently applied to both ex vivo cultured HSPCs and phenotypic HSCs.\nMore broadly, the manuscript is ambiguous about the specific stages of differentiation impacted by\nknockout of the targets in validation experiments. This needs to be more clearly discussed and\ndelineated throughout.\nWe completely agree with the Reviewer about this terminology. We have updated the manuscript\nthroughout to specify HSCs or HSPCs correctly. We have also highlighted that our cultures are a\nheterogeneous mix of HSPCs within the introduction (new text line 85).\n3. The SAGA complex contains multiple modules. Although prior studies on other subunits are\n1\nbriefly summarized in the introduction, it is perplexing to see that the other components, particularly\nthe catalytic subunits, were not included in the focused CRISPR screen or experimentally explored.\nWithout addressing these, claims about SAGA complex function remain overstated.\nWe thank the Reviewer for this comment. The Reviewer is correct that not all SAGA subunits were\nhits in the screen. To more easily understand this, we have added a new graph of the screen results for\neach SAGA subunits and also included a PyMol 3D heatmap representation (in new Figures S2a-b,\nand copied below).\nWe believe that there are a number of reasons why specific subunits of the SAGA complex did not\nappear as strong hits:\n1. Some SAGA complex subunits have functional redundancy (e.g. histone acetyltransferase\nactivity can be mediated by KAT2A or KAT2B) so that a single gene KO of one will not\nresult in a phenotype. Given the redundancy of KAT2A/B, we have performed new\nexperiments using a KAT2A/B PROTAC degrader (GSK-699), and could recapitulate many\nof the phenotypes observed in our Tada2b/Taf5l KO experiments. Additionally, we could\nconfirm a global reduction in H3K9ac (deposited by KAT2A/B) and a redistribution of\nH2Bub (the target of SAGA’s USP22 deubiquitinase). Together, these new results provide\nstrong evidence that loss of Tada2b or Taf5l lead to dysregulation of the SAGA complex\n(new Figure 5, new text lines 227-269).\n2. Certain SAGA complex subunits are more important for structural integrity. Interestingly,\nrecent bioRxiv pre-print by Batty et al. 2025 comprehensively investigated how SAGA\ncomplex subunits regulate SAGA histone acetyltransferase activity. In close correlation with\nour screen, they experimentally observed that “the non-enzymatic SAGA CORE module\nsubunits—TADA1, TAF5L, and TAF6L— as necessary for KAT2A stability, with loss of these\nsubunits disrupting the integrity of SAGA”, whereas “Targeting ATXN7, ATXN7L3, USP22\nand ENY2, components of the DUB module, did not significantly alter KAT2A levels,\nconsistent with the observation that the DUB module is loosely bound to SAGA and hence\ndispensable for its structural integrity (Herbst et al, 2021). However, knockout of TADA2B\n2\nand TADA3, components of the HAT module that are in close proximity to KAT2A within\nSAGA, resulted in a significant drop in KAT2A abundance, indicating that KAT2A protein\nlevels are reduced upon loss of its partners in the HAT module”.\nWe have added these points to the revised manuscript (new text lines 241-242) and added this pre-\nprint to our references (reference 33).\n4. Related to the previous point, given the well-established role of the SAGA complex in\ntranscriptional regulation, the impact of knockouts on chromatin biology and gene regulation relevant\nto hematopoiesis should be explored. At a minimum, given the extensive literature in this field, how\ngene expression changes observed in this setting relate to other perturbations involved in gene\nregulation that impact HSCs should be examined. Some of this can be achieved by examining\npublicly available datasets.\nWe thank the Reviewer for raising this idea. To better understand how the SAGA complex regulates\nchromatin biology and gene regulation, we have performed new experiments and analyses:\n1. We have quantified two histone marks regulated by SAGA, H3K9ac and H2Bub, by\nintracellular flow cytometry (new Figure 5c-d, S5c-d). This analysis identified that loss of\nTada2b (or Taf5l, Tada1) led to a 20-30% reduction in H3K9ac levels, but no detectable\ndifference in H2Bub levels.\n2. We performed Chipmentation to assess H3K9ac and H2Bub genomic distribution within\nTada2b-KO and control CD201+CD150+KSL HSCs (new Figures 5h, S5g-k). This identified\nsignificant changes in the distribution of H2Bub following Tada2b loss. Interestingly,\nH3K9ac was not redistributed by loss of Tada2b but enrichment was reduced.\n3. To further investigate chromatin biology, we performed ATAC-seq on Tada2b-KO and\ncontrol CD201+CD150+KSL HSCs (new Figure 5e-g). In line with a recent ATAC-see screen\nthat identified SAGA complex members as regulators of global chromatin accessibility, we\nidentified an overall reduction in the number of chromatin accessibility sites.\n4. Given the global reduction in H3K9ac levels (and recent Batty et al. bioRxiv 2025 pre-print),\nwe hypothesized that loss of SAGA histone aceyltransferase activity was a major\ndysregulated pathway in Tada2b-KO HSCs. We therefore assessed the consequences of a\nKAT2A/B inhibitor/degrader (GSK-699) on HSPCs, and could recapitulate several Tada2b-\nKO phenotypes including:\na. Reduction in H3K9ac levels (new Figure 5k)\nb. Increased immunophenotypic HSC and MkP cells in HSPCs (new Figure 5n-o)\nc. Increased Sca-1 expression within cultured HSPCs (new Figure 5m)\nd. Strong correlation in gene expression changes by RNA-seq (new Figure 5p-q)\nWe hope that the Reviewer agrees with us that these new results provide important new insights into\nhow SAGA complex members regulate HSPC activity.\n5. (sc)RNA-seq results showed upregulation of IFN genes, but the causal relationship between this\nsignature and the observed phenotype as well as the unchanged key markers in vivo remain largely\nunderdeveloped. This requires further development, as discussed in point 4 above.\nWe appreciate this Reviewer comment. To investigate the causal relationship between IFN and\nTada2b, we performed a new transplantation assay comparing Ifnar1-KO Tada2b-KO HSPCs with\nIfnar1-KO Rosa26-KO HSPCs (Figure S4i). Ifnar1-KO did not reverse the Tada2b-KO\ndifferentiation block phenotype seen in our previous Ifnar1-competent HSPC transplantation assays\n(in Figure 2). These results suggest that loss if IFNAR1 signalling alone was insufficient to rescue the\nin vivo differentiation defect. We have updated the text accordingly (new text lines 223-224).\n3\n6. The use of an MDS cell line appears a little arbitrary as there is no evidence presented that the\ncandidate genes are mutated, dysregulated, or otherwise implicated in MDS. Can better rationale for\nthis exploration be presented?\nWe thank the Reviewer for their thoughtful critique regarding the rationale for using the MDS-L cell\nline model in our study. Our use of the MDS-L model was motivated by convergent phenotypes\nbetween SAGA complex loss-of-function and hallmark features of MDS, including (1) the blocked\nhematopoietic differentiation and progenitor cell accumulation phenotypes, and (2) interferon (IFN)\npathway activation. We have updated the text to provide a more detailed justification (new text lines\n273-274).\nTo provide readers with more context, we have also added two new analyses:\n1. We re-analyzed public transcriptomic datasets from MDS patient HSPCs and confirmed an\noverlap in upregulated ISGs identified in CD34⁺ cells from MDS patients. This data is\npresented in Figure S6a and copied below.\n2. We queried the COSMIC (v97) database to assess the frequency of somatic mutations in\nTADA2B, TAF5L, TAF8, TAF13, USP22, KAT2A, and KAT2B across MDS, AML, and\nCMML patient cohorts. This analysis is summarised in Figure S6f and copied below, which\nfound that these genes are mutated at low frequencies (0.1–1.2%) across these disease\ncontexts.\nMinor Concerns:\n1. Figures need revision-1c, 3d, and 4f lack proper labeling, 1e uses incorrect nomenclature for mouse\n4\ngenes, 2g used inconsistent color schemes.\nWe thank the Reviewer for catching these figure issues and have corrected them.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank Reviewer #2 for their contribution to the peer-review process.\nReviewer #3 (Remarks to the Author):\nIn this manuscript Haney et al describe an in vivo CRISPR/Cas9 screen to evaluate genes required for\nnormal hematopoietic differentiation. They develop an ex vivo culture system to maintain HSCs\nallowing expansion and lentiviral transduction, followed by transplantation into lethally irradiated\nrecipient mice and an in vivo readout. Using this elegant system they identify components of the\nSAGA complex as mediators of hematopoietic differentiation. They show that loss of these\ncomponents leads to upregulation of the interferon response mouse hematopoietic cells. Finally, they\nshow that knockout of these genes in the MDS-L model promotes engraftment and growth of these\ncells in a xenograft model. The in vivo screening system is exciting, and the role of SAGA in\nhematopoiesis is intriguing. Overall, the paper is clearly written, although lacking somewhat in\ndiscussion, but there is a lack of clarity as to what SAGA’s role in hematopoietic differentiation is. I\nlay out these concerns plus some additional points below:\n-The details of the whole-genome screen are not clearly described. Was each sub-pool of the library\nused to infect 50 million HSCs and then transplanted into 65 recipients? Or is the 50 million cells and\n65 mice the total for the entire screen (all the sub pools combined)? Was the screen done in singlicate\nor replicate? Having specific numbers and a clearer description is helpful in assessing the\nrepresentation and potential coverage of the screen. The schematic in Figure 1a also does not contain\nmuch detail or add much in explaining how the screen was performed. One of the more novel and\nexciting aspects of this report is the whole-genome in vivo screening approach, so additional details\nare important.\nWe thank the Reviewer for pointing out that the screening methodology was not sufficiently clear in\nthe methods section. As detailed in the revised Methods section (lines 371-387), each sub-library\n(containing ~20,000 elements; total of 10 sub-libraries for the genome-wide screen) was infected into\n50 million HSPCs (at an MOI of ~0.3 to achieve 20-30% transduction), to give a coverage of 500-\n750x in vitro. Following puromycin selection and recovery, >10 million HSPCs were injected into\neach mouse cohort (1-2 million per mouse) in an attempt to maintain this coverage in vivo. The\ngenome-wide screen and the gene expression screen were performed in singlet and the top hits of\ninterest (SAGA complex) were extensively validated. We want to emphasize that while we believe\nthis to be a powerful technique, the focus of this manuscript is on the discovery of the role of the\nSAGA complex in hematopoiesis and not this screening approach as a resource or novel method.\n-1-2 million HSCs were transplanted per mouse, however generally it is thought that many fewer\nHSC’s actually contribute to adult hematopoiesis (maybe around 100,000), so it’s unclear how this\nwould impact representation within the screen. Did the authors evaluate sgRNA representation in the\npooled HSC’s prior to transplantation, or in either the HSC or mature cell pools? If possible, some\nmeasure of this should be included in the supplemental methods/data.\nWe thank the Reviewer for their concern. we now have included screen representation prior to\ntransplantation and in the mature cell pools (Figure S1b-c). The sgRNA libraries are well represented\nex vivo, but this does drop in vivo, with ~50% of sgRNAs detected. It is worth noting that this still\nrepresents ~5 sgRNAs per gene, although there is clearly a bottleneck in vivo.\nrevised Discussion (new text lines 329-340). With sgRNA library used in this study (and numbers of\nmice transplanted), we believe perfect representation in vivo is unlikely to be feasible. We believe that\nnewer barcoding sgRNA screening approaches should help improve in vivo screens, such as that\npublished by Loket e al. (Blood 2025) during these revisions. This is also discussed in our revised\nmanuscript.\n-“Here we refer to the cells in these cultures as HSCs; we acknowledge that these cultures include\nhematopoietic progenitor cells but for simplicity we will refer to these as HSC cultures.” Why not\nrefer to these as hematopoietic stem and progenitor cells (HSPCs), which would be more accurate,\nand the abbreviation is just as easy to use throughout the paper? I feel that this term is more accurate\nand will avoid potential confusion.\nWe completely agree with the Reviewer about this terminology. We have updated the manuscript\nthroughout to specify HSCs or HSPCs correctly. We have also highlighted that our cultures are a\nheterogeneous mix of HSPCs within the introduction (new text line 85).\n-Were these the only 4 members of the SAGA complex to score in the screens? What about other\nmembers of the complex or the catalytic subunit, KAT2A?\nWe thank the Reviewer for this comment. The Reviewer is correct that not all SAGA subunits were\nhits in the screen. To more easily understand this, we have added a new graph of the screen results for\neach SAGA subunits and also included a PyMol 3D heatmap representation (in new Figures S2a-b,\nand copied below).\nWe believe that there are a number of reasons why specific subunits of the SAGA complex did not\nappear as strong hits:\n1. Some SAGA complex subunits have functional redundancy (e.g. the histone\nacetyltransferases Kat2a/Kat2b) so that a single gene KO of one will not result in a\n6\nphenotype. However, to investigate the role of KAT2A/B, we have performed new\nexperiments using a KAT2A/B PROTAC degrader, and can recapitulate many of the\nphenotypes observed in our Tada2b/Taf5l KO experiments. Additionally, we could confirm a\nglobal reduction in H3K9ac (deposited by KAT2A/B) and a redistribution of H2Bub (the\ntarget of SAGA’s USP22 deubiquitinase). Together, these new results provide strong\nevidence that loss of Tada2b or Taf5l lead to dysregulation of the SAGA complex (new\nFigure 5, new text lines 227-269).\n2. Certain SAGA complex subunit are more important for structural integrity. Interestingly,\nrecent bioRxiv pre-print by Batty et al. 2025 comprehensively investigated how SAGA\ncomplex subunits regulate SAGA histone acetyltransferase activity. In close correlation with\nour screen, they experimentally observed that “the non-enzymatic SAGA CORE module\nsubunits—TADA1, TAF5L, and TAF6L— as necessary for KAT2A stability, with loss of these\nsubunits disrupting the integrity of SAGA”, whereas “Targeting ATXN7, ATXN7L3, USP22\nand ENY2, components of the DUB module, did not significantly alter KAT2A levels,\nconsistent with the observation that the DUB module is loosely bound to SAGA and hence\ndispensable for its structural integrity (Herbst et al, 2021). However, knockout of TADA2B\nand TADA3, components of the HAT module that are in close proximity to KAT2A within\nSAGA, resulted in a significant drop in KAT2A abundance, indicating that KAT2A protein\nlevels are reduced upon loss of its partners in the HAT module”.\nWe have added these points to the revised manuscript (new text lines 241-242) and added this pre-\nprint to our references (reference 33).\n-It’s a little unclear what is being proposed as a model for the function of SAGA in hematopoiesis.\nThere are potentially multiple different SAGA complexes or modules with different component\nmembers. Is the hypothesis that the components of SAGA complex identified in this screen make up a\nparticular complex important for directly regulating a subset of genes important for hematopoiesis?\nAre there published ChIPseq data for KAT2A or other SAGA complex members that might shed light\non which of these effects is direct?\nWe appreciate this comment, which stimulated us to further investigate the mechanisms underlying\nthis interesting phenotype with new experiments.\nFirst, we were stimulated by a recent pre-print from Batty et al., (bioRxiv 2025) who have found that\nTADA2B, TAFL5, and TADA1 disrupt KAT2A histone acetyl-transferase (HAT) activity and\nstability in HAP1 cells (a human cell line). We therefore quantified H3K9ac levels in control and KO\nHSPCs (new Figure 5c, S5c). We observed a significant loss of H3K9ac levels in this setting, in line\nwith disruption of SAGA HAT activity. To assess the effect on the SAGA deubiquitinase (USP22)\nactivity, we also quantified H2Bub levels, however, no change in H2Bub were observed (new Figure\n5d, S5d). We further assessed H3K9ac and H2Bub genomic distribution within Tada2b-KO and\ncontrol HSCs (new Figure 5h, S5g-i). This identified a significant redistribution of H2Bub following\nTada2b loss. Interestingly, H3K9ac was not redistributed by loss of Tada2b but enrichment was\nreduced (Figure S5j-k). Consistent with a global reduction in H3K9ac (a modification associated\nwith open chromatin), we also observed an overall reduction in open chromatin regions in Tada2b-\nKO HSCs by ATAC-seq (Figure 5e). We therefore conclude that loss of Tada2b dysregulates both\nH3K9ac and H2Bub chromatin marks, and reduces chromatin accessibility.\nSecond, we were stimulated by a recent paper by Nguyen et al (Science Advances 2024) who\nsuggested that loss of Kat2a/Kat2b in intestinal stem cells led to increased interferon activation and\nsignalling via disruption of mitochondrial integrity and activity. We therefore assessed mitochondrial\nactivity within our control and Tada2b-KO HSPC cultures. We observed a significant decrease in\nmitochondrial activity without altering overall mitochondrial cell mass (new Figure 5a, S5a-b).\nCorresponding with this partial loss in mitochondrial activity, Tada2b-KO HSPCs displayed greater\nsensitivity to low doses of a mitochondrial complex I inhibitor IACS-010759 (new Figure 5b). We\n7\ntherefore conclude that SAGA complex KOs result in reduced mitochondrial activity. We speculate\nthat this is a driver of IFN secretion and downstream IFN signalling in the Tada2b-KO setting.\nPlease note that we did also check for relevant KAT2A/SAGA ChIP-seq data but were unable to find\nany in an appropriate cell type to be directly relevant for this study.\n-The focus here is on SAGA-mediated regulation of the interferon response. However, it is not at\nclear that this is mediating the hematopoietic phenotype. Is there a hematopoietic phenotype to the\nIfnar1 knockout? Did any component of the interferon response score in the original genome-wide\nscreen? What is mediating the hematopoietic differentiation defect?\nWe thank the Reviewer for stimulating us to further interrogate the interferon response in this\nphenotype. As requested, we also rechecked our genome-wide screen for interferon response genes\nbut were unable to identify any as hits. We performed transplantation studies using Rosa26-KO\nIfnar1-KO or Tada2b-KO Ifnar1-KO HSPCs. Interestingly, the Ifnar1-KO did not rescue the Tada2b-\nKO phenotype of reduced PB output and increased Lin- bone marrow. We have added this new data\nin Figure S4i and updated the text to include this new insight (new text lines 223-224).\nAs detailed above, we have found that SAGA complex KOs disturb (1) H3K9ac and H2Bub in\nHSPCs, (2) chromatin accessibility, and (3) mitochondrial activity. It is likely that all contribute to the\nhaematopoietic differentiation defect but are difficult to disentangle experimentally. We have updated\nthe Discussion to speculate on this mechanism (new text lines 298-313).\n-What is the SAGA target that mediates the hematopoietic differentiation defect? Can the authors\ncross-reference the list of genes or pathways up- or down-regulated in the SAGA mutant cells with\nthe genes that scored in the genome-wide screen? This might provide insight into the mechanistic role\nthat SAGA plays in hematopoiesis.\nWe thank the Reviewer for the suggestion to overlap our RNA-seq DEGs with our CRISPR screen\nhits to identify mechanistic roles. Unfortunately, this analysis did not identify any overlapping genes.\nThis may well be due to the differentiation defect occurring in downstream progenitor cells while our\nRNA-seq comes from primitive HSCs.\nAs detailed above, we now have evidence that loss of SAGA complex members results in\ndysregulation of H3K9ac (new Figures 5c, S5c), H2Bub (new Figure 5d, S5d), chromatin\naccessibility (new Figure 5e), interferon signalling (Figures 3-4), and mitochondrial activity (new\nFigure 5a-b). We believe that the differentiation defect is likely to be driven by the sum of these\nmolecular perturbations and will be complex to tease apart. We have updated the discussion to\nhighlight this (new text lines 298-313).\n-The authors make the case that knockout of SAGA complex members supports the development of\nMDS/AML. To my knowledge, mutations in SAGA complex members have not been reported in\nclinical cohorts of MDS/AML. Are there any data to suggest that there is up- or down-regulation of\nthese genes during disease formation, or anything else to suggest that changes in SAGA function\nparticipate in disease biology in humans?\nWe thank the Reviewer for this comment. Our use of the MDS-L model was motivated by convergent\nphenotypes between SAGA complex loss-of-function and hallmark features of MDS, including (1) the\nblocked hematopoietic differentiation and progenitor cell accumulation phenotypes, and (2) interferon\n(IFN) pathway activation. We have updated the text to provide a more detailed justification (text lines\n272-273).\nTo provide readers with more context, we have also added two new analyses:\n8\n1. We re-analyzed public transcriptomic datasets from MDS patient HSPCs and confirmed an\noverlap in upregulated ISGs identified in CD34⁺ cells from MDS patients. This data is\npresented in Figure S6a and copied below.\n2. We queried the COSMIC (v97) database to assess the frequency of somatic mutations in\nTADA2B, TAF5L, TAF8, TAF13, USP22, KAT2A, and KAT2B across MDS, AML, and\nCMML patient cohorts. This analysis is summarised in Figure S6f and copied below, which\nfound that these genes are mutated at low frequencies (0.1–1.2%) across these disease\ncontexts.\nMinor issues:\n-Page 2, Line 65-67 (and throughout the manuscript): Mouse genes should have the first letter\ncapitalized, and the remainder should be lower case letters and then italicized. Mouse protein names\nshould have all their letters capitalized. So, “Kat2a” should be KAT2A.\nSee https://www.informatics.jax.org/mgihome/nomen/short_gene.shtmlfor further reference.\nWe thank the Reviewer for catching this error and have corrected the manuscript text here (and\nthroughout) accordingly.\n-Figure 1e-h: The gene names are capitalized here, but should be lower case.\nWe thank the Reviewer for catching this error and have corrected the figure (and other figures)\naccordingly.\n-Figure 3d: the legend says, “IFN response genes are highlighted in red.” However, no genes are\nhighlighted in red in the figure.\nunderlined” (text line 864).\n-Figure 4a: The figure suggests that there is a highly significant statistical difference between the\n“Control +TBKi” and “Tada2b KO +TBKi” conditions. However, by eye these two conditions look\nidentical. Can the authors confirm whether this is correct or not?\nWe thank the Reviewer for catching this and have corrected it. To confirm those two conditions are\nnot statistically significant.\n-Methods describing analysis of bulk RNAseq are not included in the methods section.\nWe thank the Reviewer for noting this omission and have updated the Methods section to include\ndetails of our bulk RNA-seq analysis (new text lines 510-516).\nReviewer #4 (Remarks to the Author):\nHaney et al performed a genome-wide CRISPR KO screen in ex vivo expanded murine cKit+ cells\nand following transplantation identified gene edits corresponding to members of the SAGA complex\nthat were enriched in HSPCs vs mature cells in vivo. The results were confirmed using a focused\nCRISPR screen targeting around 2000 genes. The authors focus on Tada2b and Taf5l KO and show\nan accumulation of ST-HSC/progenitors in bone marrow (but not LT-HSCs). Leveraging single cell\ntranscriptomics in CD201+CD150+KSL (ex vivo cultures), the authors show activation of IFN genes\nin KO cells compared to controls. Over expression of Tada2b dampened these signatures. Increased\nIFN secretion from cultured KO Sca1+ cells, reduced numbers of Sca1+ cells following GSK-8612\nand Tada2b edits in ifnar KO cells showed elevation of alternate sets of genes (that maintained\nelevation of HSCs) but not of IFN genes. Gene edits were then created in MDS-L cells and\naccumulation of these in xenograft BM/Spleen and activation of IFN gene sets in harvested cells.\nThis is an elegant manuscript with well-designed experiments and clear writing. The mouse\nexperiments in particularly are very well done.\nFigure 1: Was this a single expansion culture and transplant of cells divided across 65 recipients with\nmaterial pooled and sorted to identify sg enriched in stem/progenitors? If so, the schematic could be\nmade clearer. How many recipient mice were used for the 2000 gene (SAGA enriched) screen?\nWe thank the Reviewer for pointing out that the screening methodology was not sufficiently clear in\nthe methods section. As detailed in the revised Methods section (lines 371-387), each sub-library\n(containing ~20,000 elements; total of 10 sub-libraries for the genome-wide screen) was infected into\n50 million HSPCs (at an MOI of ~0.3 to achieve 20-30% transduction), to give a coverage of 500-\n750x in vitro. Following puromycin selection and recovery, >10 million were injected into each\nmouse cohort (1-2 million per mouse) in an attempt to maintain this coverage in vivo. For the\ngenome-wide screen, a total of 65 mice were transplanted. For the gene expression screen, a total of\n24 mice were transplanted. The genome-wide screen and the gene expression screen were performed\nin singlet and the top hits of interest (SAGA complex) were extensively validated. We want to\nemphasize that while we believe this to be a powerful technique, the focus of this manuscript is on the\ndiscovery of the role of the SAGA complex in hematopoiesis and not this screening approach as a\nresource or novel method.\nAs requested, we have updated the screen schematic to highlight additional details (Figure 1a).\nFigure 2: Please show the flow gating strategy used for immunophenotyping. Particularly for the BM\nLT-HSCs vs ST/LT-HSCs and pHSC/MPPs (Figure 2 c-f) given the difference in donor chimerism\n(LT-HSCs being similar PB WBCs).\nhave been added to Figure S7.\nFigure 3:\n- Phenotypic KO LT-HSCs expanded ex vivo (Fig 3a/128-130) but KO LT-HSCs were lower in vivo?\nShowing total cell expansion numbers rather than frequencies and flow cytometry gating will be\nhelpful (ditto 3i and S4g)\nWe would like to clarify that the ex vivo immunophenotypic HSC definition CD201+CD150+KSL is\nnot as functionally pure as the in vivo LT-HSC population. Serial engraftment capacity has been\nshown to be restricted to this cell fraction but only ~1:3 CD201+CD150+KSL from culture engraft in\nsingle cell transplantation assays (at a 12-week endpoint) according to Che et al., EMBO Reports\n2022. Our best guess is this ex vivo HSC population is a mixture of LT-HSCs, ST-HSCs and MPPs.\nWe would therefore not like to directly compare in vivo and ex vivo HSC frequencies, given the\ndifferences in functional heterogeneity. However, the expansion of this population ex vivo could be\ndriven by increases in downstream cell types, which are also expanded in vivo.\nWe appreciate the suggestion of presenting cell numbers rather than frequencies and representative\nflow cytometry gating. These have been added to Figures S3a and S4h, and representative gating is\nnow included in Figure S7c.\n- Comparisons were limited to HSCs in G1- was the IFN signature also increased in MPPs in G1?\nWe thank the Reviewer for this comment. We do see IFN upregulation in all clusters of our scRNA-\nseq analysis. This was already included in Tables S3-4, but not discussed in the manuscript. We have\nnow noted this insight in the main text (text lines 189-190).\n- MkP numbers appear to be increased in Tada2b and Taf5l KO?\nWe thank the Reviewer for this comment, which stimulated us to check for MkP frequencies within\nour HSPC cultures. As predicted by the scRNA-seq experiment, we did observe increases in this cell\npopulation (CD41+CD150+c-Kit+Lineage-), which are now included in a new Figure S3h.\nWe could also confirm that the same phenotype was observed following treatment with the KAT2A/B\ninhibitor/degrader GSK-699, implicating that this megakaryocyte bias was linked to the loss of SAGA\nHAT stability following loss of Tada2b or Taf5l. This new data has been added to Figure 5o.\nOverall, there is a lot of cell type nomenclature throughout this paper (MPP/HSC, pHSC, LTHSC,\nSTHSC, Mkp etc) with very little description of how these are classified (e.g. immunophenotype).\nPlease provide a table with cell type definitions or flow plots so the readers can interpret these results.\nWe appreciate the suggestion and have included a new Table to include our immunophenotypic\ndefinition of these populations (text line 449). Representative flow cytometry gating plots are also\nnow available in Figure S7.\nFigure 5: Although conceptually MDS is attractive given expansion of progenitors and impaired\ndifferentiation but it is a big leap in the absence of somatic mutations/transcriptomic evidence that\npoint to the SAGA complex in some way. The message needs to be toned down.\nWe thank the Reviewer for the comment. As requested, we have toned down this message in the\nabstract and main text. Our use of the MDS-L model was motivated by convergent phenotypes\nbetween SAGA complex loss-of-function and hallmark features of MDS, including (1) the blocked\nhematopoietic differentiation and progenitor cell accumulation phenotypes, and (2) interferon (IFN)\npathway activation. We have updated the text to provide a more detailed justification (new text lines\n272-273).\n1. We re-analyzed public transcriptomic datasets from MDS patient HSPCs and confirmed an\noverlap in upregulated ISGs identified in CD34⁺ cells from MDS patients. This data is\npresented in Figure S6a and copied below.\n2. We queried the COSMIC (v97) database to assess the frequency of somatic mutations in\nTADA2B, TAF5L, TAF8, TAF13, USP22, KAT2A, and KAT2B across MDS, AML, and\nCMML patient cohorts. This analysis is summarised in Figure S6f and copied below, which\nfound that these genes are mutated at low frequencies (0.1–1.2%) across these disease\ncontexts.\n- have you interrogated transcriptomic datasets (human MDS CD34+ vs healthy CD34+ or HR-MDS\nvs LR-MDS) for differences in SAGA complex gene expression?\nWe thank the Reviewer for this idea. However, we could not identify differential expression of SAGA\ncomplex members in human MDS transcriptomic datasets. By contrast, the same IFN genes we see\nupregulated in SAGA complex member KO HSCs have long been documented as upregulated in\nhuman MDS (e.g. Pellagatti et al 2006. Blood). This has also been reported more recently with single-\ncell RNA-seq data sets of MDS patients (Guo et al., Journal of Translational Medicine 2024).\n- MDS-L cells are an IL-3 dependent cell line generated from an MDS patient. I would not refer to\nthese as MDS cancer cells/ mouse model of human MDS. Replace all labels to clearly state that these\nare MDS-L cells in main and supplemental figures. The data are data is consistent with expansion of\nperform flow cytometry on retrieved BM-MDSL cells to interrogate their differentiation potential?\nWhat do you mean by upregulation of AML/MDS genes (Fig 4SI)?\nWe appreciate the advice and have updated the text and figures to specify these are MDS-L cells.\nRegarding MDS-L differentiation potential, we went back to frozen bone marrow samples from our\nMDS-L xenotransplantation experiment and checked several human lineage markers by flow\ncytometry: CD34 (as a stem/progenitor cell marker), CD41 (as a megakaryocyte marker) and CD33\n(as a myeloid marker). These were selected because MDS-L cells have been reported to have\nmegakaryocyte and myeloid differentiation potential. We observed high expression of all markers in\ncontrol and KO samples, with no differences identified. Given this negative data does not add\nmaterially to the manuscript, we have not included it in the revised manuscript.\nWe apologise that we were not clear regarding the upregulation of AML/MDS genes. We have\nrevised this sentence of the text (text line 286).\nMinor-\n• Line 122. S2e-f is incorrect (S2d and f).\nWe thank the Reviewer for catching this and have corrected it (text line 152).\n• Figure 1h graph heading is covering the h.\nWe thank the Reviewer for catching this and have corrected Figure 1h.\n• Figure 3a legend says two guides were used (identified with grey and black dots- only grey dots\npresent). Also says n=6, however plots show n=4 for control, n=7 for Tada2B and n=5 for Taf5I,\nplease clarify.\nWe apologise for this discrepancy. We have corrected Figure 3a with the correctly coloured dots, and\nalso updated the figure legend with the correct replicate numbers (text line 850).\n• Line 616 Figure 3d –Says IFN genes are highlighted in red, they are not. Please fix.\nWe apologise for this text error; the figure legend has been corrected to read “IFN response genes are\nunderlined” (text line 864).\n• Line 619 Figure 3d- please fix labels to: Ifi44 (f), Oasl2 (g) and Ddx60 (h).\nWe thank the Reviewer for catching this and have corrected it (text line 867).\n• Line 620 Figure 3d “E.” not sure what this is referring to, is it a mistake?\nWe apologise for this text error. This has been deleted.\n• Line 704 Figure S3 (g-h) fix label: please change to: Sca1 MF1 (g) and MHC-1 MFI (h).\nWe thank the Reviewer for catching this and have corrected it (Figure S3i-j).\n• Figure S3 (g-h) plots say ns. Is this correct? Looks like it should be significant. This is also\ndiscussed in text lines 157 and 158- implying this was significant observation.\nWe thank the Reviewer for catching this. We rechecked these statistics and the differences are non-\nsignificant. We have updated the main text to avoid confusion (text line 194).\n• Lines 174-175 and Figure 4a (lines 631-632) Both discuss changes in Sca1 MFI, however plot y-\naxis says “pHSCs frequency normalized to DSMO”?\nWe thank the Reviewer for catching this error and have corrected this plot label to “Fold change in\nSca1 MFI”.\n14"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the revised manuscript and the responses. The authors have appropriately addressed the major concerns that I had and have also done a good job of stressing the limitations of the screens that were performed. I believe the work is now appropriate for publication.", "reviewer_3": "I appreciate the authors' thoughtful response to my critiques. They have addressed all my concerns. I congratulate them on this interesting work.", "reviewer_4": "I commend the authors for the efforts taken to address comments raised at review. I am satisfied that these have been adequately addressed."}, "author_response": ""}]

Decision letter