CRISPR activation screens map the genomic landscape of cancer glycome remodeling.

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Learn more: PMC Disclaimer | PMC Copyright Notice Cell Genom . 2026 Jan 28;6(4):101139. doi: 10.1016/j.xgen.2026.101139 Search in PMC Search in PubMed View in NLM Catalog Add to search CRISPR activation screens map the genomic landscape of cancer glycome remodeling John Daly John Daly 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada Find articles by John Daly 1, 2 , Lidia Piatnitca Lidia Piatnitca 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada Find articles by Lidia Piatnitca 1, 2 , Mohammed Al-Seragi Mohammed Al-Seragi 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada Find articles by Mohammed Al-Seragi 1 , Vignesh Krishnamoorthy Vignesh Krishnamoorthy 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada Find articles by Vignesh Krishnamoorthy 1 , Simon Wisnovsky Simon Wisnovsky 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada Find articles by Simon Wisnovsky 1, 3, ∗ Author information Article notes Copyright and License information 1 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z3, Canada ∗ Corresponding author [email protected] 2 These authors contributed equally 3 Lead contact Received 2025 Mar 24; Revised 2025 Nov 10; Accepted 2026 Jan 5; Collection date 2026 Apr 8. © 2026 The Author(s) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13069869  PMID: 41610854 Summary Many cancers upregulate the expression of sialic acid-containing glycans. These oligosaccharides engage inhibitory sialic acid-binding immunoglobulin-like lectin (Siglec) receptors on immune cells, allowing cancer cells to evade immune surveillance. The genetic mechanisms underlying this process remain poorly defined. In this study, we performed gain-of-function CRISPR activation (CRISPRa) screens to define genetic pathways that regulate expression of Siglec-binding glycans. We show that Siglec ligand expression is controlled through genetic competition between genes that catalyze α2-3 sialylation and GlcNAcylation of galactose residues. Cancer glycome remodeling is also aided by the overexpression of “professional ligands” that facilitate Siglec-glycan binding. Notably, we also find that expression of the CD24 gene is genetically dispensable for cell surface binding of the inhibitory receptor Siglec-10. Finally, we identify the sulfotransferase enzyme GAL3ST4 as a potential driver of immune evasion in glioma cells. Our study provides a unique genomic atlas of cancer-associated glycosylation and identifies immediately actionable targets for cancer immunotherapy. Keywords: sialic acid, glycans, Siglec, CRISPR screening, cancer immune evasion, glycome remodeling Graphical abstract Open in a new tab Highlights • CRISPRa screening was used to identify regulators of glycosylation in cancer • Cancer glycome remodeling occurs through a diverse set of genetic mechanisms • Specific cell surface proteins drive engagement of inhibitory Siglec receptors • The sulfotransferase GAL3ST4 synthesizes immune-inhibitory glycans in glioma cells Cancer cells restructure cell surface glycosylation to evade detection by the immune system. The genes that drive this process remain poorly characterized. Here, Daly et al. use CRISPR activation screening to define the genetic basis for cancer glycome remodeling and identify new targets for stimulation of anticancer immunity. Introduction In 2018, the Nobel Prize in Medicine recognized the growing impact of immunotherapy as a strategy for treating cancer. Immunotherapies work by blocking the receptor-ligand interactions that restrain anticancer immunity. These drugs thus quench inhibitory signaling and reactivate potent anticancer immune responses. 1 , 2 , 3 Immunotherapies have produced remarkable results, even in patients with advanced cancer or otherwise untreatable disease. 4 , 5 However, many patients still fail to respond to existing immune therapies. 6 This is likely because different cancers exhibit significant heterogeneity in gene and cell surface marker expression. 7 Immune cells also express dozens of inhibitory receptors that may be relevant in different patients and cancer subtypes. 1 , 2 , 3 So far, only a few receptor-ligand pairs have approved inhibitors. 8 Characterizing new mechanisms of immune suppression in cancer is thus crucial for the development of next-generation cancer therapeutics. Changes in cell surface glycosylation can play a critical role in suppressing the anticancer immune response. 9 Immune cells express several families of receptors that bind to glycan ligands. Members of the Siglec (sialic acid-binding immunoglobulin-like lectin) family of receptors, for example, are characterized by their shared affinity for glycans that contain the sialic acid monosaccharide ( Figure 1 A). 10 , 11 , 12 Binding between a Siglec and its ligands triggers phosphorylation of immunoreceptor tyrosine-based inhibitory motifs (ITIMs) located on the intracellular face of the receptor. 10 , 11 , 12 Subsequent recruitment of the inhibitory phosphatase SHP-1 to the cell membrane represses immune activation. 10 , 11 , 12 In recent years, it has been shown that many cancers “remodel” their cell surface glycome so as to upregulate expression of glycan ligands for these Siglec receptors. 13 , 14 , 15 , 16 , 17 This process is sometimes termed cancer-associated hypersialylation and occurs in a remarkably wide cross-section of different tumor types. 18 , 19 Figure 1. Open in a new tab A CRISPR activation screening strategy for identifying genetic drivers of Siglec ligand expression (A) Diagram depicts the mechanism for inhibition of anticancer immunity by Siglec receptors. (B) The chemical structure of Siglec-7-binding O-linked glycans is depicted. (C) The strategy for overexpression of target genes by CRISPR activation (CRISPRa) is depicted. (D) Representative flow cytometry plots depict Siglec-Fc staining of K-562-CRISPRa cells. (E) Workflow of a FACS-based CRISPR screening strategy for identifying regulators of Siglec-Fc binding. Increased biosynthesis of Siglec-binding glycans (Siglec ligands) is thus a common phenomenon in cancer. However, the specific genetic mechanisms that produce this phenotype remain poorly characterized. Sialic acid is incorporated into many distinct oligosaccharide chains on cell surface glycoproteins and glycolipids. 11 Siglec-glycan interactions can also be influenced by many factors, including (1) the monosaccharide composition and stereochemistry of the glycan, (2) secondary interactions with non-carbohydrate elements of protein scaffolds, and (3) the valency of glycan presentation on the cell surface. 20 , 21 , 22 Siglec ligand biosynthesis thus requires coordinated expression and/or repression of many genes encoding specific glycan-modifying enzymes and cell surface scaffolds. Additionally, there are likely upstream oncogenes (transcription factors, kinases, etc.) that more broadly regulate glycosylation by controlling the expression of multiple elements in these polygenetic circuits. 14 , 23 Systematic identification of these key genes could provide a wealth of new targets for the development of immunotherapeutic inhibitors. Cancer glycome remodeling is thus an emergent phenotype with a highly complex genetic basis. In prior work, we began to interrogate this phenomenon using high-throughput functional genomic screening. In a pilot study, we applied a CRISPR interference (CRISPRi) screening strategy to produce an initial map of genes that are required for Siglec-7 ligand expression in hematopoietic cancer cells. 24 This study yielded several novel insights. We found that Siglec-7-Fc binding required expression of a small cluster of genes that includes the sialyltransferases (STs) ST6GALNAC1 and ST3GAL2, the glycosyltransferase (GT) C1GALT1, and the cell surface scaffolding protein CD43. Subsequent biochemical characterization revealed that Siglec-7 binds to an O-linked tetrasaccharide (the “disialyl core 1” antigen) that is synthesized by these biosynthetic enzymes 24 ( Figure 1 B). We also showed that Siglec-7/disialyl core 1 binding is enhanced through clustering of adjacent glycans on specific mucin scaffolding proteins, such as CD43 24 ( Figure 1 B). Blocking or ablating the expression of these glycans stimulated immune killing of leukemia in both cell and animal models. 14 , 24 , 25 This work thus defined a new target for leukemia immunotherapy and validated a general method to dissect the regulation of glycosylation in cancer. 26 Loss-of-function (LoF) genetic screening can be a powerful approach to identifying regulators of cell surface glycosylation. Indeed, several other studies have since applied LoF CRISPR screening to study a variety of other significant processes in glycobiology. 27 , 28 Other studies have also addressed some of these same questions using carefully designed, focused arrays of single guide RNAs (sgRNAs) against glycan biosynthesis genes. 29 , 30 However, all these methods have significant methodological limitations. Firstly, cell surface glycosylation patterns vary significantly in a cell- and tissue-dependent manner. By some estimates, only ∼50% of glycan biosynthesis genes are even expressed in any given cell model. 31 , 32 A hit discovered in one LoF screen may thus have limited generalizability to other cell types or tissues. Secondly, LoF screening may not adequately assess functionally redundant or complementary genes. This is a particular problem for factors such as GTs, which can have overlapping functions and substrate specificities. 33 Finally, genes that are essential to cell growth and viability can often be missed by traditional LoF screening. Our picture of how Siglec ligand biosynthesis is regulated in cancer cells thus remains incomplete and limited to a few select cell models. We reasoned that CRISPR activation (CRISPRa) screening would be one ideal tool for addressing these problems. In CRISPRa screening, a cell line is engineered with a dCas9 protein that is tethered to a specific transcription activation domain ( Figure 1 C). 34 , 35 Subsequent transduction of cells with an sgRNA induces selective chromatin remodeling and targeted upregulation of gene transcription. 34 , 35 CRISPRa technology allows for large-scale gain-of-function (GoF) genetic screens to be conducted in human cells. 36 CRISPRa screening has some key advantages over traditional CRISPR screening in the context of glycoscience research. Firstly, CRISPRa can transcriptionally activate many genes that have low baseline expression in a given cell line model. 36 , 37 , 38 By “forcing” overexpression of genes that would normally only be active in a specific tissue or developmental context, CRISPRa can assess a much broader range of possible regulators than LoF screening. 36 , 37 , 39 As a GoF screening technique, CRISPRa also eliminates the problems of gene essentiality and genetic redundancy that often arise in the study of glycan biosynthesis enzymes. In this study, we used CRISPRa screening technology to produce a genomic atlas of genes that regulate the expression of ligands for multiple Siglec receptors. Our key findings are outlined below. Results Genome-wide CRISPRa screens systematically map regulators of Siglec ligand expression In previous work, we and others have shown that K-562 cells express ligands for some Siglec receptors. 24 , 40 K-562s are a human erythroleukemia cell line derived from a patient with chronic myelogenous leukemia (CML). 41 K-562 cells are an ideal model for genome-wide screening: they grow rapidly in suspension, are genetically tractable, and can be sorted by fluorescence-activated cell sorting (FACS) in large numbers without loss of cell viability. 24 , 26 We therefore decided to use these cells as our model system for CRISPRa screening. K-562 cells were first transduced with constructs encoding dCas9-SunTag and VP64-scFv fusion proteins. This system allows for the recruitment of multiple VP64 transcriptional activators to specific promoter sequences, thereby upregulating expression of target genes. 34 We then incubated these cells (hereafter termed K-562-CRISPRa cells) with a panel of Siglec-Fc chimera proteins precomplexed with a fluorescent secondary antibody. For this experiment, we chose to assess Siglecs-7, -9, -10, and -15, as all these receptors have recently been implicated as potential targets for immune checkpoint blockade in various tumor types. 15 , 17 , 24 , 42 , 43 Siglecs-7, -9, and -10 displayed significant binding to K-562s, confirming that cell surface glycans are broadly hypersialylated in this cell line as observed previously ( Figure 1 D). 24 Siglec-15, conversely, showed no significant binding over background ( Figure S1 ). We therefore chose to focus our subsequent studies on Siglecs-7, -9, and -10. We transduced these K-562-CRISPRa cells with a genome-wide library of sgRNAs (∼104,000 sgRNAs, 5 sgRNAs/gene). 44 Following selection, this library was stained with fluorescently labeled Siglec-7-Fc, Siglec-9-Fc, or Siglec-10-Fc. Using FACS, we then sorted cells into either “low-staining” or “high-staining” bins. These populations corresponded, respectively, to the bottom 20% and the top 20% of the fluorescence distribution for each Siglec-Fc. Finally, we used next-generation sequencing to quantify the expression of sgRNAs in each of these populations. Using CasTLE, 45 we identified specific sgRNAs that were more abundant in either the low-staining or the high-staining population. This allowed us to generate a list of gene hits whose overexpression either increases Siglec-Fc binding or decreases Siglec-Fc binding ( Figure 1 E). The full results of all three CRISPRa screens are plotted in Figures 2 A–2C. CasTLE computes two distinct values for each gene: a CasTLE Score and an effect size. The CasTLE score is a measure of statistical significance based on the consistency of enrichment effects for different sgRNAs targeting the same gene. A higher CasTLE score indicates a greater level of statistical significance for a given hit. The effect size reflects the average difference in abundance of sgRNAs in the high- and low-staining populations. A higher effect size would indicate that gene upregulation exerts a greater impact on Siglec ligand expression. In our dataset, a positive effect size indicates that gene overexpression increased Siglec-Fc binding. These genes will subsequently be termed positive regulators. A negative effect size, conversely, suggests that gene overexpression causes a decrease in binding (negative regulators). Figure 2. Open in a new tab CRISPR activation screens reveal drivers of Siglec ligand expression (A–C) The results of CasTLE analysis for screens performed using (A) Siglec-7-Fc, (B) Siglec-9-Fc, and (C) Siglec-10-Fc are depicted. Selected genes are indicated on each graph. The CasTLE p value is indicated. (D–F) The results of Gene Ontology (GO) enrichment analysis for screens performed using (D) Siglec-7-Fc, (E) Siglec-9-Fc, and (F) Siglec-10-Fc are depicted. Glycosylation-related terms are highlighted in red. Details of all statistical analyses can be found in the STAR Methods . We recovered many hits whose overexpression altered Siglec ligand expression. Several broad patterns were immediately apparent in the data. Firstly, GO term analysis of gene hits revealed heavy enrichment of functional terms related to glycan biosynthesis ( Figures 2 D–2F). Genes encoding ST enzymes, which add sialic acid to glycans, were particularly prominent among the positive regulators identified in all three screens. We also identified several cell surface proteins that directly bind to Siglec receptors. The gene encoding CD43 (SPN), for example, was the top positive regulator discovered in our Siglec-7 CRISPRa screen. These results broadly validate the success of our new screening method. We present our full, annotated results in Table S1 , in the hope that it will be a useful resource for the glyco-immunology research community. A CasTLE p value was computed for each gene as previously described. 45 Hits were classified as statistically significant if they produced an effect with p < 0.001. This threshold corresponded approximately to a CasTLE score of >10 ( Figures 2 A–2C). Using this cutoff, we identified 941 genes whose overexpression modulated the binding of at least one Siglec-Fc. Over 50% of these hits were natively expressed at extremely low levels in K-562 cells (Human Protein Atlas, mRNA transcripts per million [TPM] < 10) ( Table S2 ). This result confirms our hypothesis that CRISPRa can be used to identify tissue-specific regulators of Siglec ligand expression that may be missed by LoF screens. This genomic atlas thus provides a significantly broader, more generalizable view of how cancer sialylation is regulated than any prior study. Below, we outline a set of the most important insights we have drawn and validated from these datasets. Siglec ligand expression is controlled by biosynthetic competition between sialylation and terminal branching/elongation of N- and O-linked glycans We began by examining gene hits that are known to play a role in glycan biosynthesis. These were identified by association of genes with GO terms related to glycan metabolism (e.g., GO0005975, carbohydrate metabolic process). Unsurprisingly, many of these biosynthetic enzymes were STs. Figure 3 A depicts the CasTLE scores for all ST enzymes encoded in the human genome. We observed that transcriptional upregulation of the ST3GAL family of STs produced particularly significant increases in Siglec ligand expression. 46 , 47 These enzymes catalyze the attachment of sialic acid onto the 3′ hydroxyl group of galactose residues. 46 , 47 This result accords with prior findings, as we and others have found that upregulation of ST3GAL enzymes drives elevated Siglec ligand biosynthesis in multiple cancer types. 13 , 17 , 48 The significance of ST3GAL genes in our screening dataset implies that galactose sialylation acts as a key “rate-limiting” step controlling expression of cell surface Siglec ligands. Figure 3. Open in a new tab Siglec ligand expression is controlled by genetic competition between glycan branching and sialylation genes (A) Heatmap depicts the CasTLE scores for the ST3GAL, ST6GAL, ST8SIA, and ST6GALNAC gene families. A positive score indicates that gene overexpression increased Siglec-Fc binding, and a negative score indicates decreased binding. (B) Heatmap depicts CasTLE scores for the GCNT, B3GNT, B3GALNT, B3GALT, and B3GAT gene families. (C) The biosynthetic pathway for the synthesis of O-linked glycans is depicted. Genes whose perturbation affected Siglec-Fc binding are indicated. (D) The biosynthetic pathway for the synthesis of N-linked glycans is depicted. (E) Heatmap depicts CasTLE scores for the FUT gene family. (F) Heatmap depicts CasTLE scores for members of the MGAT gene family. (G) Heatmap depicts CasTLE scores for genes encoding enzymes involved in CMP-sialic acid biosynthesis. ST3GAL1 and GCNT1 are shown as positive controls to normalize effect sizes. We also found that overexpression of different ST3GAL family members produced distinct effects on the binding of different Siglec-Fc reagents. For example, Siglec-7 binding was increased by overexpression of both ST3GAL1 and ST3GAL2 but not any other enzymes in the family. Siglec-9 ligand expression was more plastic, with overexpression of all ST3GAL enzymes except ST3GAL5 having a significant positive effect. Finally, Siglec-10 ligand expression was increased only by upregulation of ST3GAL4 and ST3GAL6. ST6GAL1, which attaches sialic acid to the 6′ hydroxyl of galactose, was also a hit for Siglec-10. ST6GAL1 overexpression did not affect the binding of Siglec-7-Fc or Siglec-9-Fc. These findings elegantly demonstrate that these different Siglecs bind distinct sialylated structures on the cell surface. ST3GAL1 and ST3GAL2, for example, are the primary STs that act on O-linked glycans. 46 , 47 These data thus support an emerging consensus that Siglec-7 primarily binds sialylated O-linked glycans on the surface of cells. 14 , 17 , 24 , 29 , 49 , 50 Conversely, the prominence of N-glycan-active enzymes such as ST3GAL4 and ST3GAL6 in our Siglec-10 screens implies that this receptor likely binds N-linked glycans. 13 , 17 Cancers with distinct genetic alterations in ST3GAL genes are thus likely to exhibit major differences in cell surface Siglec ligand expression, a finding with important implications for proper targeting of blocking therapeutics. Interestingly, we recovered few ST hits outside of the ST3GAL family. ST6GALNAC2 and ST6GALNAC4, which are involved in the synthesis of the disialyl-T antigen, were weak but statistically significant hits in our Siglec-7 screen. 14 , 51 , 52 This result matches prior reports showing that ST6GALNAC4 generates Siglec-7 ligands. 14 , 29 , 53 The enzyme ST8SIA6, which further modifies the disialyl core 1 epitope to generate “trisialyl-T” antigens, was also recovered as a weak hit. 54 This finding accords with another recent study showing that Siglec-7 can bind trisialyl-T structures on cell surfaces. 54 Apart from these genes, there were no other ST family enzymes that were recovered as statistically significant hits. For example, overexpression of STs involved in lipid sialylation (e.g., ST3GAL5 and ST8SIA1) produced no effects on Siglec-Fc binding. 55 , 56 These genes may thus be less important for generating Siglec ligands in cancer cells. GTs from several other families were also recovered as hits. Figure 3 B displays the CasTLE scores for all genes from the GCNT and B3GNT families, which append N-acetylglucosamine (GlcNAc) to various underlying glycan structures. 57 , 58 For these genes, the most notable effects were cases where gene overexpression significantly reduced Siglec-Fc binding. For example, upregulation of the enzymes GCNT1, B3GNT3, and B3GNT6 was found to significantly antagonize Siglec-7 ligand expression. Given what is known about the substrate specificity of these enzymes, it is likely that that such genes modify core O-glycan structures such that they can no longer be acted on by STs ( Figure 3 C). 53 , 59 Overexpression of enzymes involved in GlcNAcylation of N-linked glycans (B3GNT2, B3GNT5, and GCNT2) produced a similar dampening effect on Siglec-9 and Siglec-10 ligand expression ( Figure 3 D). 60 Many of these enzymes act on the exact same underlying substrates as the ST3GAL enzymes we identified as strong positive regulators ( Figures 3 C and 3D). “Biosynthetic competition” between ST genes and GlcNAcylation genes is thus clearly a major factor determining the overall density of Siglec ligands on the cell surface. Transcriptional dysregulation of genes at these key biosynthetic nodes is likely a major mechanism underlying cancer glycome remodeling. 61 Finally, we noted several major classes of glycan-active enzymes that were not at all represented in our list of hits ( Figures 3 E–3G). These included enzymes involved in fucosylation (the FUT family), N-glycan branching (MGAT4/5), and biosynthesis of sialic acid monosaccharide precursors (CMAS, GNE, etc.). 57 , 62 Several caveats must be noted here. Firstly, pooled genomic screening always suffers from false negatives due to variable sgRNA potency. Secondly, some biosynthetic steps may be “saturated” due to already-high expression of a specific biosynthetic enzyme in our model cell line. For example, we did not obtain ST6GALNAC1 as a strong hit, despite having shown in previous LoF studies that knockdown of this gene reduces Siglec-7 biosynthesis. This effect is likely due to the expression of ST6GALNAC1 already being so high in K-562s as to make further overexpression of genes in this pathway ineffective. 24 To aid interpretation of our screen data, we have thus tabulated the mRNA expression of each of these GTs (in K-562 cells) in Table S2 . These limitations could reasonably explain why any individual hit did not emerge from our CRISPR screen. However, we think it is implausible that these considerations can explain the absence of whole gene families (e.g., FUT enzymes) or biosynthetic pathways (CMAS and GNE) from our dataset. Our results thus strongly imply that fucosylation, core N-glycan branching, and upregulation of cytidine monophosphate (CMP)-sialic acid synthesis are all dispensable for achieving Siglec ligand overexpression in cancer cells. Upregulation of distinct “professional ligands” enhances Siglec-7 and Siglec-9 binding Some recent studies have suggested that Siglecs can exhibit preferential binding to professional ligands. Broadly defined, these are specific proteins that present glycans in an orientation or density that facilitates Siglec binding. 22 , 24 , 42 Overexpression of genes encoding these proteins may also be a mechanism underlying cancer glycome remodeling. We reasoned that our CRISPRa screening dataset might identify some of these factors. To test this hypothesis, we filtered our CRISPRa screen results for plasma membrane-localized genes that contained an extracellular domain (UniProt). We also used a more stringent cutoff (CasTLE score > 30, effect size > 3) to identify “strong” hit genes that showed significant positive selection in at least one screen. We reasoned that this step would help us prioritize the genes whose overexpression has the greatest impact on Siglec-Fc binding. These top hits are displayed in Figure 4 A. Figure 4. Open in a new tab Overexpression of specific cell surface glycoproteins increases the binding of Siglec-7 and Siglec-9 (A) Heatmap depicts CasTLE scores for top-ranked cell surface protein hits. (B) Heatmap depicts CasTLE scores for all genes in the MUC family of mucin-type O-glycoproteins. (C) Representative flow cytometry plot depicts gating strategy for K-562-CRISPRa cells transduced with a CD44 sgRNA and stained with an anti-CD44 antibody. NT sgRNA indicates cells transduced with a non-targeting sgRNA (sgNT). (D) Representative flow cytometry plot depicts staining of sgNT-transduced K-562 CRISPRa cells and CD44-overexpressing K-562 CRISPRa cells with Siglec-9-Fc. (E) The normalized median fluorescence intensity (MFI) of Siglec-9 staining in sgNT-transduced K-562-CRISPRa cells and CD44-overexpressing K-562 CRISPRa cells is shown. (F) Representative flow cytometry plot depicts staining of sgNT-transduced K-562 CRISPRa cells and CSPG4-overexpressing K-562 CRISPRa cells with Siglec-9-Fc. (G) The normalized MFI of Siglec-9 staining in sgNT-transduced K-562-CRISPRa cells and CSPG4-overexpressing K-562 CRISPRa cells is shown. (H) Graph depicts the number of N-linked and chondroitin sulfate glycosylation sites on Siglec-9 screen hits (UniProt). Statistical significance was determined using a two-tailed t test. ∗∗ p < 0.01 and ∗ p < 0.05. Mean values are plotted, and the error bars indicate the SEM. Unsurprisingly, many densely O-glycosylated proteins (mucins) were recovered as hits in our Siglec-7 screen. Our top hit was the SPN gene. This gene encodes CD43, a mucin we and others have shown interacts strongly with Siglec-7. 24 , 61 , 63 , 64 Of the five strong hits identified in our analysis, 4 genes (CD44, SPN, MUC22, and LRRC15) were found to contain mucin domains in a recent proteome-wide analysis. 65 The fifth gene (FGFR1) is a signaling receptor and so may regulate Siglec-7 ligand expression through indirect effects. 66 This trend was also confirmed by closer analysis of gene families that are known to encode mucin proteins. Several MUC genes (MUC12, MUC20, MUC21, and MUC4), for example, displayed significant positive effect sizes in our Siglec-7 screen ( Figure 4 B). This effect was not observed for all mucins, however. Indeed, overexpression of the classical mucin MUC1 actually decreased Siglec-7-Fc binding. 67 These findings are consistent with prior studies, which have shown that only specific mucins can present disialyl-T glycans at a sufficient density to mediate Siglec-7 engagement. 24 , 29 Once again, our CRISPRa screening strategy was clearly able to identify hit genes that are not natively expressed in K-562s. Such genes may thus be functionally relevant across a broader cross-section of different cancer types. MUC4, for instance, is frequently overexpressed in pancreatic cancer and has been implicated as a key driver of pancreatic cancer tumorigenesis. 68 , 69 Given recent studies showing that Siglec-7 mediates immune suppression in pancreatic cancer, our results provide strong motivation for examining this specific protein as an immunotherapeutic target. 16 , 17 Similarly, other screen hits, such as MUC21, have been implicated as adverse prognostic markers in diseases such as glioblastoma and melanoma. 70 Our dataset is thus a useful resource for characterization of novel Siglec-7 ligands in carcinomas, where known ligands such as CD43 are unlikely to be expressed. 61 The results of our Siglec-9 screen displayed a markedly different pattern. Here, there was no tendency for O-glycosylated proteins to be enriched as screen hits. Indeed, overexpression of many of the MUC genes, as well as SPN, decreased binding of Siglec-9-Fc ( Figures 4 A and 4B). However, we did identify a distinct set of hits known to localize at the cell surface. Overall, 6 genes met our cutoff for statistical significance: LRRC15, CD44, CSPG4, CSPG5, ANPEP, and SCARB1. These genes have never been described as Siglec-9 ligands. We therefore sought to confirm that overexpression of these genes increases Siglec-9-Fc binding. We transduced K-562-CRISPRa cells with sgRNAs against the three most significant of these six top hit genes: CD44, CSPG4, and LRRC15. We then stained cells with fluorescent antibodies against each of these targets. In general, sgRNA transduction resulted in a marked increase in average protein expression, although with considerable intercellular heterogeneity ( Figure 4 C). Because of this breadth in staining intensity, we next chose to co-stain cells with Siglec-Fc reagents and protein-targeted fluorescent antibodies. This step allowed us to selectively analyze Siglec-Fc binding to cells that showed a significant increase in expression of the given cell surface protein ( Figure 4 C). We found that overexpression of CD44 and CSPG4 significantly enhanced the binding of Siglec-9-Fc ( Figures 4 D–4G). For LRRC15, we found that sgRNA transduction increased non-specific binding to our human Fc (hFc) control, indicating that this hit is likely non-specific ( Figure S2 ). CSPG4 and CD44 may thus represent novel professional ligands for Siglec-9. It is unclear what shared feature is possessed by CSPG4 and CD44 that makes them bind to Siglec-9-Fc so strongly. We and others have previously shown that Siglec-9-Fc binding depends primarily on expression of complex N-linked glycans. 13 , 29 , 71 Unsurprisingly, both proteins have several N-linked glycosylation sites. Additionally, we noticed that three of our five valid hits (excluding LRRC15) possess annotated chondroitin sulfate (CS) modification sites ( Figure 4 H). This result is notable, as CS is a rare type of glycosylation. 72 At this point, it is not clear whether CS may play a role in facilitating Siglec-9 binding. We did not uncover any CS biosynthesis genes in either our CRISPRa screens or our previously published LoF screens. 24 We therefore think it is unlikely that CS is required for Siglec-9-Fc binding or that Siglec-9 binds directly to CS. However, CS modification may cause these proteins to adopt a conformation that exposes N-linked glycans for Siglec-9 binding. Regardless, the identification of these two proteins as Siglec-9 ligands provides a strong starting point for further investigation into Siglec-9’s protein binding selectivity. 72 Siglec-10 does not selectively bind CD24 but displays broad affinity for multiple classes of N-linked sialoglycans One of the most surprising aspects of our dataset was a negative result: the absence of the cell surface protein CD24 as a hit in our Siglec-10 screen. Multiple prior studies have reported that Siglec-10 binds selectively to CD24. 42 , 73 We thus expected that overexpression of this gene would enhance Siglec-10-Fc binding. As CRISPRa screens are prone to false negatives, we confirmed this result using a targeted assay. We selected two sgRNAs against the CD24 gene from our genome-wide library, transduced them into K-562-CRISPRa cells, and stained cells with an anti-CD24 fluorescent antibody. Both sgRNAs produced significant upregulation of cell surface CD24 expression ( Figure 5 A). We then assessed whether CD24 overexpression was associated with increased Siglec-10-Fc binding. Surprisingly, non-transduced K-562-CRISPRa cells bound strongly to Siglec-10-Fc but did not express CD24 at detectable levels. CD24 overexpression also produced no change in Siglec-10-Fc binding ( Figure 5 A). To further confirm this result, we stained another hematopoietic cell line (THP-1) with anti-CD24 and Siglec-10-Fc. These cells also expressed Siglec-10 ligands but did not show any binding to our CD24 antibody ( Figure 5 B). These results show that CD24 expression is not required for Siglec-10 binding. Figure 5. Open in a new tab Siglec-10 displays broad specificity for N-linked sialoglycans (A and B) Representative flow cytometry plots depict staining of (A) K-562-CRISPRa cells and (B) THP-1 cells with an anti-CD24 antibody and Siglec-10-Fc. (C) The biosynthetic pathways for sialylation of N-linked glycans are shown. (D) Heatmap depicts the average mRNA expression of key glycosyltransferase hits in cell lines derived from B cell acute lymphoblastic leukemia (B-ALL), T cell acute lymphoblastic leukemia (T-ALL), acute myeloid leukemia (AML), and multiple myeloma. (E) Representative flow cytometry plots depict staining of OCI-AML-2 WT and ST3GAL4 KO cells with Siglec-10-Fc. (F) The normalized MFI of Siglec-10 staining in OCI-AML-2 WT and ST3GAL4 KO cells is shown. (G) Representative flow cytometry plot depicts co-staining of OCI-AML-2 MGAT1 KO cells with L-PHA and Siglec-10-Fc. (H) Graph indicates MFI of Siglec-9-Fc and Siglec-10-Fc staining in the WT (L-PHA+) and MGAT1 KO (L-PHA−) populations. (I) Representative flow cytometry plot depicts co-staining of OCI-AML-2 C1GALT1 KO cells with DBA and Siglec-10-Fc. (J) Graph indicates MFI of Siglec-9-Fc and Siglec-10-Fc staining in the WT (DBA−) and C1GALT1 KO (DBA+) populations. (K) Representative flow cytometry plot depicts staining of MM1S ST6GAL1 WT and KO cells with Siglec-10-Fc. (L) The normalized MFI of Siglec-10 staining in MM1S WT and ST6GAL1 KO cells is shown. (M) Representative flow cytometry plot depicts staining of sgNT-transduced K-562 CRISPRa cells and CD44-overexpressing K-562 CRISPRa cells with Siglec-10-Fc. (N) The normalized MFI of Siglec-10-Fc staining in sgNT-transduced K-562 CRISPRa cells and CD44-overexpressing K-562 CRISPRa cells is shown. (O) Representative flow cytometry plot depicts staining of sgNT-transduced K-562 CRISPRa cells and CSPG4-overexpressing K-562 CRISPRa cells with Siglec-10-Fc. (P) The normalized MFI of Siglec-10-Fc staining in sgNT-transduced K-562 CRISPRa cells and CSPG4-overexpressing K-562 CRISPRa cells is shown. Statistical significance was determined using a two-tailed t test. ∗∗ p < 0.01 and ∗ p < 0.05. NS, not significant. Mean values are plotted, and the error bars indicate the SEM. We thus interrogated what types of sialoglycans are bound by Siglec-10. As mentioned above, our screen identified multiple STs whose activity boosts Siglec-10-Fc binding. Two strong hits were ST3GAL4 and ST3GAL6, which add sialic acid to terminal Galβ1,4-GlcNAc structures to generate Neu5Acα2,3-Galβ1,4-GlcNAc. 46 , 74 Interestingly, the other ST that showed a strong positive effect was ST6GAL1. This enzyme also acts on Galβ1,4-GlcNAc to generate Neu5Acα2,6-Galβ1,4-GlcNAc ( Figure 5 C). ST6GAL1 only emerged as a hit in our Siglec-10 screen ( Figure 3 A), not in our Siglec-9 screen. Indeed, it was one of the only GTs that showed markedly different effects in the Siglec-9 and Siglec-10 screens, which otherwise produced remarkably similar hits ( Figure 3 D). These results imply that Siglec-10 can bind glycans that terminate with either a 2,3-linked or 2,6-linked sialic acid. To confirm this result, we next assessed how CRISPR-Cas9 knockout (KO) of these STs affects Siglec-10 ligand expression. Relative expression of ST3GAL4, ST3GAL6, and ST6GAL1 varies significantly across different cell and tissue types. To help select appropriate models for these experiments, we analyzed mRNA expression of these genes in over 100 blood cancer cell lines (Human Protein Atlas). As we have previously described, acute myeloid leukemia (AML) cell lines showed elevated expression of the gene ST3GAL4 relative to other genes in the ST family ( Figure 5 D). 13 We thus hypothesized that ST3GAL4 is likely the key driver of Siglec-10 ligand expression in AML. We transduced a representative AML cell line (OCI-AML-2) with Cas9 and an sgRNA against ST3GAL4. We have shown in prior studies that ST3GAL4 KO reduces Siglec-9-Fc binding in OCI-AML-2 cells. 13 We confirmed successful gene KO (over 90% editing) using tracking of indels by decomposition (TIDE) analysis 13 , 75 ( Figure S3 A). KO of ST3GAL4 significantly reduced Siglec-10-Fc binding, demonstrating that Siglec-10 can indeed bind to glycans containing Neu5Acα2,3-Galβ1,4-GlcNAc ( Figures 5 E and 5F). Conversely, KO of ST6GAL1 had no effect on Siglec-10-Fc binding in OCI-AML-2 cells ( Figures S3 A, S4 A, and S4B). In prior work, we have shown that ST3GAL4 KO primarily affects sialylation of N-linked glycans in AML cells. 13 Our results thus strongly imply that Siglec-10 binds N-linked glycans. To confirm this hypothesis, we transduced OCI-AML-2 (AML) cells with Cas9 and an sgRNA against MGAT1, an essential enzyme in the synthesis of complex N-linked glycans. 76 Cells were then co-stained with L-PHA, a plant lectin that binds N-linked glycans, and Siglec-10-Fc. 76 MGAT1 KO cells were identified by gating on the cell population that exhibited reduced L-PHA staining ( Figure 5 G). These MGAT1 KO cells displayed a complete ablation of Siglec-10 ligand expression ( Figure 5 H). In parallel, we also transduced cells with an sgRNA against C1GALT1, a key enzyme in the elongation of O-linked glycans. 76 Here, C1GALT1 KO cells were identified by co-staining with the lectin DBA, which binds truncated O-glycans ( Figure 5 I). 76 C1GALT1 KO caused no change in Siglec-10-Fc staining ( Figure 5 J). Together, these data confirm that Siglec-10 displays remarkable selectivity for binding N-linked sialoglycans. As an aside, our experiments with MGAT1 KO and C1GALT1 KO cells also revealed a subtle difference between the glycan-binding specificities of Siglec-9 and Siglec-10. We and others have found that Siglec-9 primarily binds to N-linked sialoglycans on most cell lines. 13 , 29 Indeed, MGAT1 KO reduced Siglec-9-Fc binding quite significantly, as expected ( Figure 5 H). However, Siglec-9-Fc still showed some residual binding to MGAT1 KO cells. This result contrasted with Siglec-10, where MGAT1 KO eliminated all detectable binding over background. Similarly, C1GALT1 KO caused a small but reproducible decrease in Siglec-9-Fc binding ( Figure 5 J) but had no effect on Siglec-10-Fc binding. This result aligns with our screen data, where overexpression of several O-glycan-active enzymes (ST3GAL1, ST3GAL2, and GCNT1) affected Siglec-9-Fc binding but not Siglec-10-Fc binding. It is thus likely that Siglec-9 can bind some O-linked glycans, even if its preferred ligands are N-linked. Siglec-10, conversely, seems to exclusively bind N-linked structures. We next explored the role of ST6GAL1 in Siglec-10 ligand expression. In contrast to AML, we found that multiple myeloma (MM) cell lines expressed much higher levels of ST6GAL1 than ST3GAL4 ( Figure 5 D). Hypersialylation has also been implicated as a driver of immune evasion in MM. 49 Here, we reasoned that ST6GAL1 was likely to be the key ST involved in Siglec-10 ligand biosynthesis. We therefore transduced a representative MM cell line (MM1S) with Cas9 and an sgRNA targeting ST6GAL1. Cells were then stained with the lectin SNA, which binds 2,6-linked sialic acids, and sorted via FACS to yield a purified ST6GAL1 KO population ( Figure S5 ). 76 , 77 ST6GAL1 KO induced a strong decrease in Siglec-10-Fc binding ( Figures 5 K and 5L). In contrast to our results in AML, ST3GAL4 KO had no effect on Siglec-10-Fc binding in MM1S cells ( Figures S3 B, S4 C, and S4D). These results confirm that different STs are likely to drive overexpression of Siglec-10 ligands in different cancer types. Lastly, we assessed whether Siglec-10 binds any distinct ligands other than CD24. Surprisingly, we were not able to identify a single cell surface protein hit in our Siglec-10 screen. The only gene that passed our statistical cutoff was LRRC15, which we had previously shown is a non-specific hit whose overexpression increases hFc binding ( Figure S2 ). To confirm this result, we used CRISPRa to overexpress the two genes (CSPG4 and CD44) that we had already identified as professional Siglec-9 ligands. In contrast to our results with Siglec-9, we found that overexpression of these genes had little effect on Siglec-10-Fc binding ( Figures 5 M–5P). These results suggest that Siglec-10 exhibits quite broad affinity for many N-linked glycoproteins, such that overexpression of any given protein has a minimal effect on binding. The sulfotransferase enzyme GAL3ST4 drives Siglec ligand expression in glioma cells Finally, we developed an unbiased hit prioritization pipeline to identify any high-value cancer immunotherapy targets that may have been missed by our previous analyses. As described above, our CRISPRa screen identified dozens of genes whose overexpression increases cell surface binding of inhibitory Siglec receptors. We hypothesized that if one of these genes was a significant driver of immune evasion in a specific cancer subtype, then higher expression of that gene would likely be associated with poor patient survival in that cancer. We thus cross-referenced our list of top positive regulators (CasTLE score > 30) with a recent study that used The Cancer Genome Atlas (TCGA) to identify adverse prognostic factors across 33 different cancer types. 78 We first assessed whether elevated mRNA expression of our positive regulators was significantly associated with worse patient survival. The details of our analysis are fully described in the STAR Methods . The full results are provided in Table S3 . This step revealed several interesting insights. First, high expression of our top ST hits was not strongly associated with poor patient survival in any cancer type. Secondly, several cell surface ligands (e.g., CD44 in renal cell carcinoma) were identified as key adverse prognostic markers. Finally, we identified a remarkably strong association between expression and patient survival for one gene that we had not previously examined. Low-grade glioma (LGG) patients with high mRNA expression of the GAL3ST4 gene showed accelerated disease progression and poor survival when compared to patients with low expression ( Figure 6 A). We also observed a similar trend when we examined copy-number variations, where amplification of the GAL3ST4 gene was associated with poor prognosis ( Figure 6 B). Finally, we found that methylation of the GAL3ST4 promoter region also predicted better patient survival ( Figure 6 C). These data thus provided a strong impetus to investigate GAL3ST4 as a possible driver of immune evasion in glioma. Figure 6. Open in a new tab GAL3ST4 drives Siglec-7 ligand expression in glioma cells (A–C) Graphs depict Kaplan-Meier survival analysis for (A) low-grade glioma (LGG) patients with either high or low mRNA expression of the GAL3ST4 gene, (B) LGG patients with either a high or low chromosomal copy number at the GAL3ST4 locus, and (C) LGG patients with either high or low DNA methylation at the GAL3ST4 promoter. (D) The biosynthetic pathway for either sialylation (ST3GAL1/2) or sulfation (GAL3ST4) of core 1 O-linked glycans is depicted. (E) The average mRNA expression of ST3GAL1, ST3GAL2, and GAL3ST4 across 14 different cancer types is shown. (F) Representative flow cytometry plot depicts staining of the indicated LN-229 cell lines with PNA. (G) Representative flow cytometry plot depicts staining of the indicated LN-229 cell lines with Siglec-7-Fc. (H) Representative flow cytometry plots depict staining of sialidase-treated LN-229 cells with Siglec-7-Fc. (I) The potential structure of a novel sulfated Siglec-7 ligand is depicted. Several recent studies demonstrated that Siglecs (particularly Siglec-9) are strongly upregulated on tumor-infiltrating myeloid cells in glioma and glioblastoma. 15 , 79 Inhibiting murine Siglecs also significantly reduced cancer progression in preclinical models of this disease. 15 , 79 However, the genetic perturbations that drive recruitment of Siglec-expressing immune cells have not been well-defined. To study the role of GAL3ST4 in this process, we analyzed TCGA data to assess whether GAL3ST4 expression correlated with infiltration of Siglec-expressing immune cells in LGG. We compared mRNA expression of GAL3ST4 with that of Siglec-7 and Siglec-9 in a cohort of LGG patient samples. As Siglecs typically exhibit immune-restricted expression, we assumed that any mRNA expression of these Siglecs in a tumor sample would be derived from infiltrating immune cells and not tumor cells. 11 We found a strong correlation between GAL3ST4 expression and Siglec expression in these patients with glioma ( Figures S6 A and S6B). Our data thus imply a specific link between GAL3ST4, Siglec ligand expression, and immune evasion in LGG. GAL3ST4 is a sulfotransferase that appends sulfate to galactose. 80 , 81 The substrate specificity of GAL3ST4 has been extensively characterized in prior work. In vitro , GAL3ST4 was found to sulfate the 3′ hydroxyl of galactose in the core 1 O-glycan structure (Gal-β1,3-GalNAc) ( Figure 6 D). 81 ST3GAL1 and ST3GAL2 act on the same functional group, meaning that these enzymes likely compete with GAL3ST4 ( Figure 6 D). GAL3ST4’s activity is quite selective, as it showed no ability to sulfate other acceptor substrates, such as Gal-β1,4-GlcNAc. 81 GAL3ST4’s specificity for O-linked glycans has subsequently been confirmed in cellulo in a more recent study. 80 This gene emerged as a particularly strong hit in our Siglec-7 screen, where it was the top-ranked positive regulator of Siglec-7-Fc binding. The finding that overexpression of sulfotransferases can drive Siglec ligand expression is supported by several prior studies. 29 , 82 The specific physiological context in which glycan sulfation is relevant for Siglec binding, however, has not yet been established. We therefore used the Human Protein Atlas to examine the average mRNA expression of GAL3ST4, ST3GAL1, and ST3GAL2 in 14 different cancers ( Figure 6 E). GAL3ST4 expression was very low in most cancers (average TPM < 5). ST3GAL1 and/or ST3GAL2 expression exceeded that of GAL3ST4 in almost all cases, indicating that sialylated core 1 structures are likely the dominant Siglec-7 ligands in most cell types. Interestingly, the only strong exception to this trend was in glioblastoma, where GAL3ST4 expression significantly surpassed that of ST3GAL1/2. These data imply that GAL3ST4 may be a key tissue-specific driver of Siglec-7 ligand expression in brain cancer cells. To test this hypothesis, we used the Human Protein Atlas to select a brain cancer cell line model (LN-229) with representative expression of GAL3ST4. We subsequently transduced this cell line with Cas9 and an sgRNA targeting GAL3ST4. Successful editing at the GAL3ST4 locus was confirmed by TIDE analysis ( Figure S7 ). We also stained GAL3ST4 KO cells with fluorescently labeled peanut agglutinin (PNA), which binds to unmodified core 1 O-glycans. 83 GAL3ST4 KO produced a large increase in PNA binding, indicating that this enzyme does modify O-linked glycans in glioma cells ( Figure 6 F). We then stained wild-type (WT) and GAL3ST4 KO cells with our Siglec-Fc chimeras. We observed a striking reduction in Siglec-7 ligand expression, with GAL3ST4 KO reducing Siglec-7-Fc binding to ∼20% of WT levels ( Figure 6 G). Re-transfection of these cells with WT GAL3ST4 restored Siglec-7-Fc binding to WT levels, confirming the specificity of this effect. To our knowledge, this study is the first to demonstrate that the KO of a specific sulfotransferase enzyme can ablate Siglec binding in a cancer cell model. Interestingly, while GAL3ST4 was also a hit in the Siglec-9 CRISPRa screen, GAL3ST4 KO did not significantly impact Siglec-9-Fc binding in glioma cells ( Figure S8 ). As discussed above, our screen results show that Siglec-9-Fc binding can be increased by overexpression of many different GTs (GAL3ST4, ST3GAL1, ST3GAL2, ST3GAL3, ST3GAL4, and ST3GAL6). In practice, some cancer cell lines will express multiple enzymes from this list at high levels. If so, the KO of one GT may not produce a significant effect on Siglec-9-Fc binding, as other GTs will still be highly active. LN-229s, indeed, express the enzyme ST3GAL4 at very high levels (Human Protein Atlas). Given the proven role of ST3GAL4 in generating Siglec-9 ligands, it is likely that this enzyme plays the dominant role in Siglec-9 ligand generation in glioma cells. 13 , 17 Treatment of LN-229 cells with a 2,3-specific sialidase largely ablated Siglec-9-Fc binding ( Figure S9 ), providing support for this hypothesis. These findings suggest that Siglec-7 can bind core 1 O-glycans that are sulfated at the galactose residue. Next, we wanted to better characterize how Siglec-7 might interact with these sulfated O-glycans. The canonical Siglec-7 ligand we have previously characterized is the disialyl core 1 O-glycan ( Figure 1 B). In most cells, this structure is generated by ST3GAL1 (which generates the Neu5Ac-α2,3-Gal linkage) and ST6GALNAC4 (which synthesizes Neu5Ac-α2,6-GalNAc). 14 In LN-229 cells, it appears that the galactose residue of the core 1 structure is sulfated rather than sialylated ( Figure 6 D). However, the GalNAc residue in this structure may still be available for sialylation. Several prior studies have also shown that the presence of this 2,6-linked sialic acid is critically important for Siglec-7-Fc binding. 14 , 29 We therefore hypothesized that the Siglec-7-binding O-glycans in LN-229 cells likely contain 2,6-linked sialic acids. To test this hypothesis, we treated LN-229 cells with a sialidase that selectively cleaves 2,3-linked sialic acids, as well as a broadly acting sialidase that cleaves all linkages. Treatment with the 2,3-specific sialidase had no effect on Siglec-7-Fc binding. Conversely, the broadly acting sialidase still eliminated Siglec-7-Fc binding ( Figure 6 H). These data confirm that Siglec-7 still binds sialic acids on glioma cells that are not 2,3-linked. Considering prior work, our results tentatively suggest that Siglec-7 may bind a novel “3-sulfo-6-sialyl core 1” structure ( Figure 6 I). Our data are not definitive, however, and this claim would have to be further confirmed by structural biology studies (see limitations of the study ). Discussion A broad conclusion of our study is that there is no single gene whose dysregulation drives glycome remodeling across all cancers. The specific perturbation(s) required to achieve overexpression of Siglec-binding glycans will depend on the underlying transcriptional state of the healthy tissue. There are many genetic “paths” by which cancers may acquire a hypersialylated (or hypersulfated) phenotype, and these are likely to differ between tumor types and patients. In many cases, upregulation of specific STs may drive increased Siglec ligand expression. This has recently been demonstrated in several clinical contexts. 13 , 17 However, silencing of key glycan branching enzymes may be an equally potent mechanism for enhancing Siglec ligand expression in other tissues. Understanding these complex dynamics is becoming more important, as Siglec-blocking therapeutics are now being tested in clinical trials. 15 , 42 , 43 , 84 , 85 Currently, there are few reliable methods for identifying patients who are most likely to benefit from these therapeutics. Our CRISPRa dataset provides a multitude of possible biomarkers. In the future, our work could be used to guide the development of predictive gene expression models that can rapidly identify patients with cancer likely to overexpress specific Siglec ligands. Our screens also showed that Siglec binding is regulated by the expression of key professional ligands that present glycans on the cell surface. These genes are all cell surface localized and thus accessible to biologic blocking therapeutics. Our dataset thus provides a wealth of targets for future characterization in specific disease models. Our results also provide deeper insight into the glycan-binding specificities of different Siglec receptors. Particularly interesting questions remain about how Siglec-7 interacts with its ligands on cell surfaces. 29 , 82 We found that overexpression of the GAL3ST4 sulfotransferase enhanced Siglec-7-Fc binding. Therefore, one might think that Siglec-7 binds directly to sulfate groups. However, we would strongly caution the reader—it is not possible to draw this conclusion solely from gene KO and overexpression experiments. This is because O-glycan biosynthesis involves complex crosstalk between different glycan-active enzymes. Our results and other studies suggest that Siglec-7 binding depends on the presence of Neu5Ac-α2,6-GalNAc motifs within O-glycans. 14 , 29 Some of the ST6GALNAC family enzymes that make this structure prefer core 1 substrates that have already been modified at the galactose residue. 86 The activity of these enzymes may thus be dependent on the co-expression of genes such as GAL3ST4. 2,3-linked modification of core 1 O-glycans can also inhibit GCNT1 activity, further facilitating 2,6-sialylation of the GalNAc residue. 87 It is thus possible that GAL3ST4 overexpression indirectly increases Siglec-7-Fc binding by driving O-glycan biosynthesis toward structures containing Neu5Ac-α2,6-GalNAc. Regardless of the mechanism, our results do clearly show that GAL3ST4 expression is genetically crucial for generating Siglec-7 ligands in glioma cells. This is the core claim of our study. Whether Siglec-7 makes physical contacts with sulfated galactose residues remains to be determined. Relatedly, another key finding from our study is that expression of the cell surface protein CD24 is not required for Siglec-10-Fc binding. These data contradict previous work indicating a connection between these two proteins. 7 , 42 Our results do not invalidate that prior work, which was largely conducted in quite different cells and model systems. 42 , 73 For example, it is possible that epithelial cell lines selectively express co-receptors that are important for Siglec-10/CD24 binding. However, we do think that our results urge a more precise re-examination of Siglec-10’s specificity for different types of glycoprotein ligands. In this context, our discovery that ST6GAL1 overexpression can drive Siglec-10 binding is particularly notable. ST6GAL1 overexpression is observed in several types of cancer. 88 , 89 This enzyme drives tumorigenesis through a variety of mechanisms that are still being elucidated. 88 , 89 Our results suggest that ST6GAL1 overexpression may drive increased production of Siglec-10 ligands and suppression of anticancer immunity. Finally, our work identifies GAL3ST4 as a possible driver of immune evasion in glioma. This enzyme has several appealing properties as a potential drug target. Firstly, the gene is selectively upregulated in glioma patient samples and is a clear adverse prognostic factor. This contrasts with other glycan-active enzymes, for which we found it difficult to find prognostic associations in clinical data. Secondly, expression of this enzyme is quite tissue restricted relative to the other glycan-active enzymes (ST3GAL1, ST3GAL2, ST6GALNAC4, etc.) that generate Siglec-7 ligands. In principle, pharmacological inhibition of this enzyme may have fewer off-target effects than comparable enzymes. While small-molecule sulfotransferase inhibitors have been explored as drug candidates, there are currently no known specific inhibitors of GAL3ST4. 90 If these cannot be developed, our data also imply that blocking Siglec-7 may be an effective strategy for attacking gliomas with high expression of GAL3ST4. These directions represent significant opportunities for future work. Limitations of the study We acknowledge several limitations of our study. Firstly, this work has focused primarily on characterizing the genetic drivers of Siglec binding to cancer cells. In some cases, we have used these data to make inferences about the likely biochemical structures of the ligands that are bound by individual Siglecs. However, we have not directly assessed molecular interactions between Siglecs and synthetic forms of these proposed glycan ligands. The absence of such direct molecular characterization may create opportunities for misinterpretation of screening data. Relatedly, drawing conclusions about the structure of Siglec ligands from the effects of GT KO experiments is most reasonable when the substrate specificities of those GTs are well characterized. However, some GTs have only been studied in vitro , and different studies sometimes report subtly different findings. 57 This limitation may also make it difficult to draw firm conclusions about the biochemical structure of Siglec-binding glycans. The reader should bear these facts in mind when interpreting our results. Lastly, we have not included any data characterizing the effects of hit gene KO on Siglec signaling in immune cells in an intact tumor microenvironment. Given the large number of genes and cancer types we discuss, we felt that focusing attention on any one hit as a target would be premature at this stage. Specific hits of interest deserve to be given full, detailed attention in future work from our and other labs. While we strongly suspect that many of our hit genes will be interesting therapeutic targets, the translational implications of these findings are unproven. In our view, the most important current bottleneck in this field is the lack of well-validated preclinical models for studying Siglec biology. Importantly, Siglec receptors are not conserved in mice, and recent studies have also shown that cellular glycosylation patterns are also dramatically different within the murine immune system. 91 Following up on any one hit is thus a complex undertaking that involves extensive model construction and characterization. Nevertheless, we view this as the most exciting direction for future work. Resource availability Lead contact Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Simon Wisnovsky ( [email protected] ). Materials availability This study did not generate new, unique reagents. Data and code availability No original code was generated in this manuscript. All computational tools used in this manuscript are detailed in the key resources table . Acknowledgments S.W. acknowledges funding support from the National Science & Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research, the Canadian Glycomics Network, the Cancer Research Society, the Canadian Cancer Society, and Health Research BC. Author contributions S.W. designed and supervised the study. J.D., L.P., M.A.-S., and V.K. performed data collection and analysis. S.W., J.D., and L.P. wrote and revised the manuscript. All authors have reviewed and approved the manuscript. Declaration of interests The authors declare no competing interests. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies PE- anti -human-CD24 (Clone W20001B) BioLegend 382609; RRID: AB_2936718 Human LRRC15 Antibody R&D Systems MAB11324-SP; RRID: AB_3657512 FITC anti-human CD44 antibody BioLegend 397517; RRID: AB_2888763 NG2 Monoclonal Antibody (9.2.27), Alexa Fluor™ 488 Thermo Scientific (eBioscience) 53-6504-80; RRID: AB_10853004 PE-Mouse IgG2b,k Isotype Control BioLegend 402203; RRID: AB_3097041 Bacterial and virus strains Stellar™ Competent Cells Takara Bio 636763 Oligonucleotides Table of sgRNAs used in this study This study Table S4 Table of primers used in this study This study Table S5 Chemicals, peptides, and recombinant proteins Alpha MEM Thermo Scientific 12571-063 TransIT-LT1 (LT1 transfection reagent) Mirus Bio MIR2304 Siglec-7-Fc (carrier-free) R&D Biosystems 1138-SL Siglec-9-Fc (carrier-free) R&D Biosystems 1139-SL Siglec-10-Fc (carrier-free) R&D Biosystems 2130-SL Alexa Fluor® 647 AffiniPure™ Goat Anti-Human IgG Jackson ImmunoResearch 109-605-006 Recombinant Human IgG1 Fc (Thr106-Lys330) BioLegend 773006 α2,3-specific sialidase Lectenz GE0302-5KU Pan-sialidase (Vibrio cholerae) Millipore Sigma 11080725001 Dolichos Biflorus Agglutinin (DBA), FITC Thermo Scientific L32474 Lectin PHA-L (Phaseolus vulgaris), Alexa Fluor™ 594 Thermo Scientific L32456 Sambucus Nigra Lectin (SNA), Fluorescein Thermo Scientific L32479 PNA Lectin, Alexa Fluor 488 Conjugate Thermo Scientific L21409 RPMI-1640 medium Thermo Scientific 11875–119 DMEM, high glucose Thermo Scientific 11995–065 Fetal Bovine Serum Thermo Scientific 12483–020 Polybrene (hexadimethrine bromide) Millipore Sigma TR1003G DPBS (-Ca, -Mg) Thermo Scientific 14190–250 Puromycin (10 mg/mL solution) InvivoGen ant-pr-1 Blasticidin (10 mg/mL solution) InvivoGen ant-bl-05 RetroNectin-coated plates Takara Bio T100A BstXI New England Biolabs R0113L BlpI New England Biolabs R0585L T4 DNA Ligase New England Biolabs M0202S Critical commercial assays GeneJET Gel Extraction Kit Thermo Scientific K0691 GeneJET Genomic DNA Purification Kit Thermo Scientific K0721 MycoStrip Tests Thermo Scientific REP-MYS-20 Herculase II Fusion DNA Polymerase Kit Agilent Technologies 600675 FastDigest ESP3I Thermo Scientific FD0454 Experimental models: Cell lines MM1S ATCC CRL-2974 OCI-AML-2 DSMZ ACC 99 LN-229 ATCC CRL-2611 THP-1 ATCC TIB-202 Lenti-X 293T cell Line Takara Bio 632180 K-562 ATCC CCL-243 Recombinant DNA Human CRISPRa-v2 pooled library (half-library, top 5 sgRNAs/gene) Addgene 3978 Vector backbone pCRISPRia-v2 Addgene 84832 lentiCRISPR v2 Addgene 52961 GAL3ST4-pTwist-Lenti-CMV-BSD Twist Bioscience N/A psPAX2 plasmid Addgene 12260 pMD2.G plasmid Addgene 12259 lentiCas9-Blast plasmid Addgene 52962 Software and algorithms CasTLE Morgens et al. 45 https://github.com/elifesciences-publications/dmorgens-castle Guide-Counter GitHub https://github.com/fulcrumgenomics/guide-counter GORilla Eden et al. 92 https://cbl-gorilla.cs.technion.ac.il/ TIDE Brinkman et al. 93 https://tide.nki.nl/ Open in a new tab Experimental model and study participant details Cell culture K-562 (female), MM1S (female), LN-229 (female) and THP-1 (male) cells were acquired from American Type Culture Collection (ATCC). Lenti-X HEK293-T (female) cells were acquired from Takara Bio. OCI-AML-2 cells were acquired from DSMZ. K562, THP-1 and MM1S were cultured in Roswell Park Memorial Institute 1640 (RPMI-1640) medium supplemented with 10% fetal bovine serum (FBS) at 5% CO2 and 37°C. OCI-AML-2 (male) and Lenti-X HEK293T cells were cultured in Alpha-MEM media supplemented with 10% FBS at 5% CO 2 and 37°C. LN-229 cells were cultured in DMEM containing 5% FBS at 5% CO2 and 37°C. All cell lines were sub-cultured at frequencies and concentrations recommended by the supplier. All cell lines were screened periodically for mycoplasma contamination using MycoStrip tests. Cell lines were not otherwise authenticated. Method details Cloning of sgRNAs Vectors were digested with BsmBI (lentiCRISPR v2) or BstXI/Blp1 (pCRISPRia-v2). The linearized fragment was gel purified using the GeneJET Gel Extraction Kit according to manufacturer’s instructions. For each sgRNA, complementary oligonucleotides were ordered from Integrated DNA Technologies (IDT) with single stranded overhangs complementary to the sticky ends of the digested vector. Ligation was then performed using T4 DNA Ligase according to manufacturer’s instructions. Transformation was performed using RecA- Stellar Competent Cells to avoid lentiviral rearrangements. Individual clones were screened by Sanger sequencing and verified by next-generation sequencing (Plasmidsaurus). A table of sgRNA sequences used in this study is given in Table S4 . Unless otherwise indicated, cDNAs were ordered cloned into pTwist-Lenti-CMV-BSD directly from the vendor (Twist Biosciences). Lentiviral packaging For lentiviral packaging of individual sgRNA constructs (pCRISPRia-v2, lentiCRISPR v2) or cDNA constructs (LentiCas9-blast, GAL3ST4-pTwist-Lenti-CMV-BSD), Lenti-X cells were plated onto six well plates at a cell concentration of 0.4x10 6 cells/well in 2.5 mL of medium and allowed to adhere overnight. For each well, 2 μg of sgRNA library plasmid was mixed with 2 μg total lentiviral packaging plasmids (1 μg PMD2.G and 1 μg psPAX2) in 0.25 mL of serum-free DMEM. 8 μL of Mirus-LT1 transfection reagent was separately diluted in 0.25 mL of serum-free DMEM and allowed to sit at room temperature for 5min. The two solutions were then combined, and the mixture was allowed to incubate at room temperature for 30 min. The total volume of each transfection complex was then carefully added dropwise to each well. During this process the media on the plate was swirled slowly but constantly. Cells were then placed back in the incubator and virus production was allowed to proceed for 72h. Lentiviral media was then removed from each plate and centrifuged at 3000xg for 10min to clear any cellular debris. Lentiviral media was then snap frozen and stored at −80°C until the day of library infection. For lentiviral packaging of whole genome-CRISPR libraries, Lenti-X cells were plated onto 150 mm dishes at a cell concentration of 7.5x10 6 cells/dish in 30mL of medium and allowed to adhere overnight. CRISPRa sublibraries h1-h7 (Human CRISPRa-v2 pooled library) were mixed together in proportions weighted to the number of sgRNAs in each sublibrary to a total of 8 μg total. For each plate, 8 μg of sgRNA library plasmid was then mixed with 8 μg total lentiviral packaging plasmids (4μg PMD2.G and 4μg psPAX2) in 2mL of serum-free DMEM. 48 μL of Mirus-LT1 transfection reagent was separately diluted in 2mL of serum-free DMEM and allowed to sit at room temperature for 5min. The two solutions were then combined, and the mixture was allowed to incubate at room temperature for 30min. The total volume of each transfection complex was then carefully added to each 150mm plate dropwise. During this process the media on the plate was swirled slowly but constantly. Cells were then placed back in the incubator and virus production was allowed to proceed for 72h. Lentiviral media was then centrifuged and stored as above. Lentiviral transduction For individual sgRNA transductions, cells were seeded at recommended subculture concentrations (ATCC) in appropriate culture media for a total volume of 2mL in a 6-well cell culture plate. 500μL of viral supernatant (or standard culture media as a control) was added to the cells along with 8μg/mL of polybrene. The culture was gently mixed and incubated at 37°C for 48 h after which the cells were collected and centrifuged for 5 min at 600xG. The supernatant was removed, and cells were resuspended in 3mL of appropriate culture media containing 1 μg/mL puromycin or 5 μg/mL blasticidin before incubation at 37°C. Media was replaced every 48h along until there were no viable cells in the control well. For transductions involving MM1S cell, RetroNectin-coated plates were used to enhance transduction efficiency according to the manufacturer’s instructions. For ST6GAL1 KO experiments in this cell line, cells were stained with SNA as described below (see lectin staining ). SNA − cells were sterile sorted by FACS, counted, washed and re-suspended in culture medium at 5% CO 2 and 37°C. These cells were cultured until aliquots were frozen or until needed in downstream phenotyping assays. For genome-wide CRISPRa library transduction, 2.5 × 10 8 K562-CRISPRa cells growing in log phase were spun down and resuspended in 500mL of complete media containing 8μg/mL polybrene and a volume of lentiviral media previously determined to give a multiplicity of infection (MOI) of 0.35. MOI was quantitated in a smaller scale experiment by determining the titer of lentiviral media that produced 30% BFP expression (as determined by flow cytometry) 72h following infection. Cells were then selected with 1μg/mL of puromycin for 48 h. Cells were then aliquoted in 10 separate 150 mm plates (50mL per plate). After 24 h, cells were centrifuged at 600xG and resuspended in media containing 1 μg/mL of puromycin at 5x10 5 cells/mL. Following an additional 48 h of puromycin selection, cells were spun down and resuspended again in fresh media containing 1 μg/mL of puromycin, maintaining a cell density of 5x10 5 cells/mL. After 96h of selection, viability of the infected cell population was greater than 95%, while an uninfected control plate similarly treated with puromycin had cellular viability less than 1%. After this point, cells were spun down and resuspended in fresh media without puromycin and expanded. Cell staining and sorting were performed within 5 days of removing cells from puromycin selection. Lectin staining Fluorescently labeled Siglec-Fc chimeras were prepared by pre-complexing 1.5 μg/mL of Siglec-7Fc, Siglec-9Fc or Siglec-10Fc chimeras with 1.5μg/mL Alexa Fluor 647 AffiniPure Goat Anti-Human IgG in 1X PBS for 45min on ice in the dark. As a control 1.5μg/mL Recombinant Human IgG1 Fc was pre-complexed with Alexa Fluor 647 AffiniPure Goat Anti-Human IgG under the same conditions. Target cells were often co-stained with fluorescently labeled Siglec-Fc chimeras and lectins. In these cases, L-PHA-AF594 (5μg/mL), DBA-FITC (10μg/mL) or PNA-FITC (5 μg/mL) were added to Siglec-Fc precomplexes during incubation. Target cells were also co-stained with antibodies against specific cell surface markers. In these cases, antibodies were diluted 1:100 from their original stock into the precomplex solution. Meanwhile, cells of interest were collected, counted, washed with 1X PBS and aliquoted into 96-well V-bottom culture plates before being centrifuged at 600xG for 5min. Supernatant was discarded and cells were then resuspended in appropriate pre-complex solution at 1x10 5 cells/100μL (for K562, THP-1, MM1S and LN-229 cells) or 2x10 5 cells/100μL (for OCI-AML-2 cells) Cells were incubated for 30 min on ice. Cells were subsequently centrifuged at 600xG for 5min, after which supernatant was discarded, cells were washed with 1X PBS, resuspended in 1X PBS and samples were run on either an LSR II (BD Biosciences) or a CytoFLEX (Beckman Coulter) flow cytometer. FSC vs. SSC was used to gate intact cells and FSC-A vs. FSC-H was used to exclude doublets. When plasmids encoding a BFP marker were used, cells were further gated to analyze solely BFP + cells before expression. A minimum of 5,000 single, intact cells were recorded for each sample. For experiments where cells were treated with sialidases, cells were harvested at 5 × 10 6 cells/mL and incubated with either pan-sialidase (0.1 U/mL in DMEM +5% FBS) or α2,3-specific sialidase (Lectenz, 5000 U/mL in DPBS +2% FBS) for 45 min at 37°C in 5% CO 2 , with gentle resuspension every 15 min. Following treatment, cells were washed once with ice-cold DPBS +2% FBS and stained for analysis by flow cytometry as described above. FACS sorting of genome-wide CRISPRa libraries Siglec-7, Siglec-9 and Siglec-10 CRISPRa screens were performed in duplicate. All replicates were maintained in culture at a library coverage of 1000x (∼1x10 8 cells/replicate). Prior to sorting, 2μg/mL of Siglec-7Fc, Siglec-9-Fc or Siglec-10Fc was pre-complexed with 2μg/mL of Alexa Fluor 647 AffiniPure Goat Anti-Human IgG as described above (see lectin staining ). For each CRISPRa genome-wide screen replicate, 1.2x10 8 cells were then pelleted at 600xG, washed once with 1X PBS and resuspended in precomplex solutions at 5x10 6 cells/mL. Cells were incubated on a rocker table in a cold room for 45min to ensure consistent but gentle mixing of the cells. Following staining, cells were spun down at 600xg, washed twice with 1X PBS and resuspended in 1X PBS. Cells were then passed through a 70μm nylon cell strainer (Corning Incorporated) to remove aggregates and then placed on the sorter arm, where they were kept at 4°C and rotated frequently for the duration of the sort. Cell sorting was performed on a BD FACSAria II. Cells were selected by gating on FSC vs. SSC (to exclude debris), FSC-A vs. FSC-H (to exclude doublets) and FSC-H vs. BV421 (to exclude BFP − cells) parameters. Finally, gates on Siglec-Fc staining were constructed such that cells were only sorted if they exhibited a fluorescence intensity value placing them in either lowest or highest quintile of fluorescence intensity for that specific Siglec-Fc fluorescence distribution. Sorted cells were periodically pelleted as they came off the soter. DNA from sorted samples was immediately isolated using the Thermo GeneJet Genomic DNA Miniprep Kit according to manufacturer’s instructions and flash frozen prior to future processing. Library amplification, sequencing, and data processing Libraries were amplified via nested PCR and sequenced on an Illumina NextSeq as previously described. 26 Following demultiplexing, FASTQ files from each sample were aligned to the reference CRISPRa library using guide-counter to generate a read count table. Read count tables were then analyzed using CasTLE 45 (Cas9 High T hroughput maximum L ikelihood E stimator) 45 to identify sgRNAs that exhibited altered abundance in low-staining and high-staining samples. If sgRNAs targeting a given gene were more abundant in the high-staining sample than the low-staining sample, the gene was classified as a hit with a positive effect score. If sgRNAs targeting a given gene were more abundant in the low-staining sample than the high-staining sample, the gene was classified as a hit with a positive effect score. Gene Ontology (GO) enrichment analysis Enriched GO terms in each screen were identified by ranking all gene hits by CasTLE Score (both positive and negative hits). This ranked list was then analyzed using GORilla. 92 A false discovery rate cutoff of 0.01 was used to identify statistically significant GO terms. This filtered list was then ranked by fold enrichment prior to displaying data. TCGA analysis Genome-wide survival scores for all top screen hits were accessed using a previously published resource 78 ( https://www.tcga-survival.com/ ). In this survival model, all genes are assigned a Z score in each cancer type. A higher Z score indicating that genetic variation in that gene has prognostic significance. Gene copy number, methylation and mRNA expression were all analyzed separately. Genes with a Z score below −5 or above 5 in at least one cancer type were prioritized. mRNA expression analysis mRNA expression data for human cell lines was sourced from the Human Protein Atlas ( proteinatlas.org ). mRNA expression data for clinical samples was sourced from the TCGA ( cbioportal.org ) using the Brain Lower Grade Glioma (TCGA, PanCancer Atlas) dataset. TIDE analysis Genomic DNA was extracted from transduced cell lines using the GeneJET Genomic DNA Purification Kit (ThermoFisher, K0721) according to the manufacturer’s protocol. Fragments containing the sgRNA target sites were subsequently amplified via PCR with the Herculase II Fusion DNA Polymerase kit. The resulting PCR products were resolved on a 1% agarose gel, purified using the GeneJET Gel Extraction Kit and Sanger Sequenced. Sequence data from wild-type and knockout samples were analyzed with TIDE software, 93 which quantifies insertion and deletion events at the target locus through deconvolution of Sanger sequencing traces. A table of primer sequences used in this study is given in Table S5 . Quantification and statistical analysis Information on statistical tests used, n values, dispersion measures, precision measures and definitions of significance are all provided in the relevant figure captions. No randomization or data exclusion was performed. Two-tailed Student’s t-tests were performed using Prism. CRISPR screen p -values were determined using CasTLE. 45 Published: January 28, 2026 Footnotes Supplemental information can be found online at https://doi.org/10.1016/j.xgen.2026.101139 . Supplemental information Document S1. Figures S1–S9 and Tables S4 and S5 mmc1.pdf (893.2KB, pdf) Table S1. Combined results of CRISPR activation screens mmc2.xlsx (5.2MB, xlsx) Table S2. RNA expression profiles of top hit genes and glycosyltransferase genes in K-562 cells mmc3.xlsx (37.5KB, xlsx) Table S3. Prognostic association scores of hit genes across different cancer types mmc4.xlsx (35.5KB, xlsx) Document S2. Article plus supplemental information mmc5.pdf (15.7MB, pdf) References 1. Munoz-Wolf N., Lavelle E.C. Innate Immune Receptors. Methods Mol. Biol. 2016;1417:1–43. doi: 10.1007/978-1-4939-3566-6_1. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Kawai T., Akira S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. 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Article plus supplemental information mmc5.pdf (15.7MB, pdf) Data Availability Statement No original code was generated in this manuscript. All computational tools used in this manuscript are detailed in the key resources table . 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