Effect of eosinophils on T-cells in differently active Brain tumors

Effect of eosinophils on T-cells in differently active Brain tumors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effect of eosinophils on T-cells in differently active Brain tumors Li Chang, Yu Yiwen, Zhu Yibei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635903/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the effect of eosinophils on T-cells in various active Brain tumors. Methods By analyzing the SDY1637 CyTOF data in Immport database, we observed the differences in immune cell composition and the correlation between eosinophils and T-cells function in different tumor models by applying CyTOF technology, heatmap and clustering analysis, and t-SNE downscaling visualization. Results The proportion of eosinophils and effector/memory T-cells was higher in immunoreactive Brain tumors, there was spatial synergy between them, and eosinophils promoted T-cells activation and memory phenotype differentiation; regulatory T-cells and depleted T-cells dominated in immunoinert Brain tumors, and eosinophils deficiency led to T-cells depletion. After tumor resection, eosinophils rebounded and remodeled the immune microenvironment. Conclusion Eosinophils have a significant impact on T-cells function in different active Brain tumors, targeting eosinophils is expected to enhance anti-tumor immunity and provide a new direction for Brain tumors therapy, and the molecular mechanism of the interaction between the two remains to be further investigated. Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Tumour immunology Eosinophils T-cells Brain tumors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Brain tumors are serious diseases of the nervous system, with increasing morbidity and mortality rates and a variety of types, among which gliomas are the most common and highly malignant. Conventional treatments are limited by the specific location and biological characteristics of Brain tumors, and the search for new therapies has become a hot topic. Eosinophils are derived from bone marrow hematopoietic stem cells, which play a key role in the immune system, and their role in the tumors microenvironment is of increasing interest. In a variety of solid tumors, eosinophils inhibit tumors growth and metastasis in the early stages but promote tumors progression in the later stages, and also interact with tumors-associated macrophages to regulate the immune status of the tumor microenvironment. T-cells (T lymphocyte) are central in tumor immunity and are classified into CD4⁺ helper T-cells (Th cells) and CD8⁺ cytotoxic T-cells (CTLs). Th cells can differentiate into different subpopulations, each with different functions. CD8⁺ CTL is the main effector cell for killing tumor cells. However, tumor cells can evade immune attack through multiple mechanisms. Enhancing T-cells anti-tumor activity and overcoming immune escape are key to tumor immunotherapy. Cytokines secreted by eosinophils can affect T-cells activation, proliferation and differentiation, and the chemokines they secrete can recruit T-cells, as well as interact with T-cells and regulate T-cells function through surface molecules. It is of great significance to study the effect of eosinophils on T- cells in Brain tumors. On the one hand, it can help to reveal the immune escape mechanism of Brain tumors, provide a new perspective to understand the dynamic changes of immune cells and the regulation of immune balance in the tumor microenvironment, and lay a theoretical foundation for the development of new strategies for immunotherapy. On the other hand, based on the interaction between the two, new therapeutic targets can be found, such as regulating eosinophils function to enhance the anti-tumor activity of T-cells, or blocking the immunosuppressive signals to break the immune escape of tumors, which can improve the therapeutic effect of Brain tumors. 2 Material and Methods 2.1 Material This study utilized the SDY1637 CyTOF data from the Immport database ( https://www.immport.org/shared/home ). Only eosinophils and expression of different T-cells surface markers were included for the analysis of the study. The data sources covered different tumors models, such as GL261 and 005 in the Active group, and CT2A and Mut3 in the Inert group, which provided a rich sample base for studying the role of eosinophils on different T-cells subpopulations in Brain tumors environment. 2.2 Methods Immune cell subpopulations were analysed by CyTOF technology to clearly define different cell subpopulations. Using this technique it was possible to precisely reveal differences in the proportions of immune cell subpopulations, such as the percentage of eosinophils in different active tumors groups and the distribution of different T-cells subpopulations in different tumors models. In addition, the cell percentages of different T-cells subpopulations in the two datasets (JK6 and JK7) were analysed, and the characteristics as well as spatial distributions of immune cells in different tumor models were explored in depth with the help of heatmap and cluster analyses, as well as the visualisation of the t-SNE downscaling. 2.3 Observation indicators Differences in immune cell composition were mainly observed in different tumor models. The enrichment of immune response pathways (e.g. antigen presentation, T-cells activation) in GL261 and 005 tumors, as well as the expression of cell cycle-related genes in CT2A and Mut3 tumors were observed by RNA sequencing analysis. Among them, the immune response pathways were significantly enriched in GL261 and 005 tumors (P < 0.001), suggesting higher immunoreactivity, while the high expression of cell cycle-related genes in CT2A and Mut3 tumors suggested their immune inert character. Meanwhile, the proportions of eosinophils in different active tumors groups (Active and Inert groups) were observed, as well as the distribution of various types of T-cells subpopulations (e.g. effector T-cells and regulatory T-cells in CD4⁺ helper T-cells, memory T-cells and depleted T-cells in CD8⁺ cytotoxic T-cells, etc.) in different distribution characteristics in different tumor models. In addition, attention was paid to the clustering distribution and spatial location relationships of immune cells in the t-SNE downscaling visualisation maps as a means of analysing the functional synergistic or inhibitory relationships between cells, as well as the correlation between eosinophils and T-cells functions, such as the association between the proportion of eosinophils and the changes in the number of different T-cells subpopulations. 2.4 Statistical methods Data were analysed using statistical methods. When determining differences in data between different groups, differences were determined to be statistically significant or not by setting significance levels (e.g. * for P < 0.05, * for *P < 0.01, *** for P < 0.001). For example, this method was applied to make judgements when comparing the differences in the proportions of eosinophils in the Active and Inert groups, as well as the differences in the percentages of different T-cells subpopulations in the different datasets (JK6 and JK7), so as to provide reliable data to support the conclusions of the study. 3 Results 3.1 Differences in immune cell composition in different tumors models The immune cell composition of different tumor models varied significantly, and analysis by RNA sequencing revealed that immune response pathways (e.g. antigen presentation, T-cells activation) were significantly enriched in the GL261 and 005 tumors (P < 0.001), suggesting higher immunoreactivity, whereas the high expression of cell cycle-related genes in the CT2A and Mut3 tumors was suggestive of an immunoinert character (Fig. 1 ) CyTOF technique further revealed differences in the proportions of immune cell subpopulations, in which the proportion of eosinophils was significantly higher in the Active group (GL261: 4.5 ± 0.8%; 005: 5.2 ± 1.1%) than in the Inert group (CT2A: 1.3 ± 0.4%; Mut3: 0.9 ± 0.3%, P < 0.01), suggesting that eosinophils are closely related to the tumor immunoreactivity were closely related (Fig. 1 ). Regarding the distribution of T-cells subsets, a higher proportion of CD4 + effector T-cells (12.3 ± 2.1%) and CD8 + memory T-cells (8.7 ± 1.5%) were found in the Active group, whereas CD4 + regulatory T-cells (Treg, 15.6 ± 3.2%) and CD8 + depletion T-cells (PD-1 + , 22.4 ± 4.1%) predominated in the Inert group. The association between immunoreactivity and T-cells functional status was further validated (Table 1 ). Table 1 Characteristics of T-cells subpopulation distribution(Data analysis source https://rdcu.be/eeT6v ) Tumor model Classification of immune activity Eosinophil ratio(%) T-cells subset characteristics (percentage) percentage(%) GL261 Immune activity 4.5 ± 0.8 CD103 + CD4 + tissue-resident memory T- cells 18.2 Depletion of CD8 + T cells(PD-1 + CD39 + ) 5.1 CD4 + effector T-cells(CD44 + CD62L − ) 12.3 ± 2.1 CD8 + memory T-cells(CD44 + CD62L − ) 8.7 ± 1.5 5 Immune activity 5.2 ± 1.1 Classical CD8 + T-cells(CD44 + CD62L − ) 9.3 Depletion of CD8 + T-cells 3.8 CD4 + effector T-cells(CD44 + CD62L − ) 12.3 ± 2.1 CD8 + memory T-cells(CD44 + CD62L − ) 8.7 ± 1.5 CT2A Immune inert 1.3 ± 0.4 Depletion of CD8 + T-cells(PD-1 + CD39 + ) 22.4 CD39 + immunosuppressive T-cells 25.7 CD4 + regulatory T-cells(FoxP3 + CD25 + ) 15.6 ± 3.2 Mut3 Immune inert 0.9 ± 0.3 CD4 + regulatory T-cells(FoxP3 + CD25 + ) 15.6 ± 3.2 Classical CD8 + T-cells(CD44 + CD62L − ) 12.9 In addition, by further analysing the cell percentages of different T-cells subpopulations in the two datasets (JK6 and JK7) (Fig. 3 and Fig. 4 ), the differences in T-cells subpopulations could be understood in more detail. In terms of depleted T-cells, the percentages of ‘Exhausted CD4 + T-cells’ and ‘Exhausted CD8 + T-cells’ in the JK6 dataset were higher than those in the JK7 dataset, and the differences were highly statistically significant (P < 0.001). Among the initial, effector and memory T-cells subpopulations, all subpopulations except ‘Memory CD4 + T Cell’ were also significantly different between the JK6 and JK7 data sets, and in most cases, the percentage of cells was higher in the JK6 data set than in the JK7 data set. These results further reflect the differences in the between the functional status of T-cells in the tumors microenvironment and tumors immunoreactivity distribution of T-cells subpopulations in different samples, which can help to deeply understand the relationship. Heatmap and cluster analysis (Fig. 5 ) showed that Active group samples (e.g., JK1.01) had significantly high expression in CD4 + initial T-cells (Z-score = 2.5) and CD8 + effector T-cells (Z-score = 3.0), whereas the Inert group samples (e.g., JK6.01) had expression of eosinophil-associated genes (Z-score = 1.2) lower, suggesting significant differences in immune cell profiles across tumors models. Together, these results suggest a higher proportion of eosinophils and effector/memory T-cells in immunoreactive tumors, whereas Treg and depleted T-cells predominate in immunoinert tumors. 3.2 Correlation of eosinophils with T-cells function Correlation analysis of eosinophils and T-cells function showed that in the Active group, the proportion of eosinophils was significantly and positively correlated with CD103 + CD4 + T-cells (r = 0.72, P < 0.05) and CD8 + effector T-cells (r = 0.65, P < 0.05), suggesting that eosinophils promote T-cells activation and memory phenotypic differentiation; while in the Inert group, eosinophil reduction was accompanied by an increase in PD-1 + CD8 + T-cells (r=-0.81, P < 0.01), suggesting that eosinophil deficiency leads to T-cells depletion. In addition, after CT2A tumors resection, the proportion of eosinophils significantly increased from 1.3–3.8% (P < 0.05), along with an increase in the proportion of CD8 + effector T-cells from 7.1–14.5%, and an increase in the proportion of SiglecF + macrophages from 8.2–15.6%, whereas the number of quiescent microglial cells was reduced (Fig. 6 ), suggesting that eosinophils after tumors removal contribute to T-cells depletion through regulation of the macrophage and T-cells functions to remodel the immune microenvironment and enhance the anti-tumor immune response. 4 Discussion 4.1 Synergy between eosinophils and T cells In this study, we applied t-SNE downscaling analysis and found that there was a spatial synergistic relationship between eosinophils and T-cells in the tumors microenvironment. t-SNE results showed that the spatial distribution of eosinophils was similar to that of CD4 + memory T-cells and CD8 + effector T-cells. This implies that eosinophils can regulate the function of neighbouring T-cells by secreting cytokines such as IL-5 and GM-CSF, which can promote T-cells proliferation and differentiation, and GM-CSF, which can enhance the activation and effector function of T-cells, and eosinophils can regulate the metabolism of T-cells through cell-to-cell contact. This spatial synergy was significant in immunologically active tumors, and heat map analysis also confirmed that CD8 + effector T-cells in the Active group had a Z-score value of 3.0 and were functionally active, correlating with a high proportion of eosinophils (4.5 ± 0.8%) and spatial co-localisation, as well as a high proportion of CD4 + memory T-cells (18.2%). This provides new evidence for the role of eosinophils in anti-tumor immunity and a theoretical basis for the development of eosinophil-based immunotherapeutic strategies, such as local delivery of IL-5 or GM-CSF to enhance anti-tumor immune responses. 4.2 Characterisation of depleted T-cells in immunologically inert tumors The proportion of depleted T-cells was significantly higher in immunologically inert tumors up to 20%, with an isolated spatial distribution and suppressed function due to lack of eosinophil paracrine support. T-cells subpopulations varied across samples, with a higher proportion of depleted T-cells in JK6, a high percentage of initial, effector T cells in JK6, and a complex memory T-cells profile. Eosinophils promoted T-cells activation and proliferation in immunoreactive tumors, and the proportion was significantly lower in immunologically inert tumors (CT2A: 1.3 ± 0.4%; Mut3: 0.9 ± 0.3%), resulting in T-cells entering a depleted state. Depleted T-cells highly expressed PD-1 and CD39, PD-1 inhibited T-cells activation and proliferation, and CD39 hydrolysed ATP to generate adenosine to form an immunosuppressive microenvironment. In immunoinert tumors, the proportion of PD-1 + CD8 + T-cells reached 22.4 ± 4.1%, and the expression level of CD39 was 25.7%, which was consistent with the immune escape mechanism of clinical Brain tumors, suggesting the key role of depleted T-cells in tumors progression and immunotherapy resistance, for which immunotherapeutic strategies targeting the PD-1/CD39 axis could be developed. 4.3 Eosinophils as regulators of immunoreactive tumors In immunoreactive tumors (Active group: GL261, 005), eosinophils are key regulators. It secretes cytokines such as IL-5 and GM-CSF, which significantly promote CD103 + CD4 + memory T-cells expansion (18.2%) and inhibit CD8 + T-cells depletion (the proportion of PD-1 + decreased to 3.7%).IL-5 activates the STAT5 signaling pathway to promote T-cells survival and memory phenotype differentiation, and GM-CSF enhances T-cells effector function through the JAK-STAT and PI3K- AKT pathways to enhance T-cells effector function. Eosinophils also regulate T cells through intercellular contacts or exosomal signaling. In contrast, in immunologically inert tumors (CT2A, Mut3), eosinophils were significantly absent (CT2A: 1.3 ± 0.4%; Mut3: 0.9 ± 0.3%), and the immune microenvironment was altered and exacerbated by the predominance of Treg (15.6%) and depleted T-cells (22.4%).Treg secretion of TGF-β and IL-10 suppressed effector T-cells, and depleted T cells with high expression of PD-1 and CD39 mediated the immunosuppressive microenvironment, consistent with clinical Brain tumors immune escape mechanisms. Therefore, targeted modulation of eosinophil number and function, such as local delivery of IL-5 or GM-CSF, can help to reverse immunosuppression and provide a new strategy for GBM immunotherapy. 4.4 Mechanisms of immune remodelling after tumors resection Eosinophil rebound (1.3%→3.8%) plays a key role in the remodeling of the immune microenvironment after tumors resection. It recruits SiglecF + macrophages to synergistically activate T-cells and promote postoperative immune recovery. SiglecF + macrophages have strong antigen presentation and immunomodulatory functions, secrete pro-inflammatory factors such as IL-12 and TNF-α to enhance the activation and proliferation of T-cells, and eosinophils also act on T-cells through exosomes or cytokines to promote their differentiation to effector or memory phenotypes. Previous studies have shown that chemokines such as CCL5 released by eosinophils are important in enhancing T-cells infiltration, and in the present study, eosinophil rebound after CT2A tumors resection was associated with a significant increase in CD8 + effector T-cells (7.1% → 14.5%), and eosinophils also released chemokines such as CXCL9 and CXCL10 or cytokines such as IL-4 and IL-13 modulating the immune microenvironment. These provide new ideas for the development of chemokine-based postoperative immunotherapy strategies, such as local delivery of CCL5 or CXCL10 to reduce the risk of tumors recurrence. 4.5 Clinical translation potential Targeting eosinophils provides a new strategy for Brain tumors immunotherapy. In immunologically inert tumors, local delivery of IL-5 to activate eosinophils may reverse T-cells depletion, reduce the proportion of PD-1 + CD8 + T-cells from 22.4% to less than 10%, and enhance anti-tumor immune responses. CyTOF-based heat map analysis may provide a basis for patient stratification, with a high Z-score (> 2.5) of CD4 + initial T-cells predicting a better immunotherapy response and contributing to the development of a personalised treatment plan. These reveal the potential value of eosinophils in GBM immunotherapy and point to new directions for developing therapeutic strategies based on immune microenvironmental characteristics. 5 Conclusion Eosinophils inhibit depletion and promote memory phenotypes by regulating T- cells differentiation in immunoreactive GBM, whereas their absence in immunoinert tumors results in an immunosuppressive microenvironment. Targeting eosinophils may enhance anti-tumor immunity and provide new directions for GBM therapy. The sample size is small (only n = 1 in the MostA ctive/Most Inert group) and needs to be expanded for validation; the molecular mechanisms of eosinophil-T-cells interactions (e.g., specific receptors) have not yet been clarified. Declarations Ethical Approval and Consent to participate Not applicable Consent for publication The undersigned authors of the manuscript entitled "Effect of eosinophils on T-cells in differently active Brain tumors" confirm that we consent to the publication of this work in Molecular Medicine. We irrevocably transfer all copyright ownership of the manuscript, including any tables, figures, or supplementary materials, to the publisher upon acceptance. We authorize the publisher to reproduce, distribute, display, and archive the manuscript in all forms, including print, electronic, and digital formats, and to make such versions available to the public in accordance with the journal’s policies. The authors retain the right to use the manuscript for personal or institutional purposes, such as storing it in their own or their institution’s repository, provided proper attribution to the original publication is included. We declare that the manuscript is original, has not been published elsewhere, and is not under consideration for publication in another journal. All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Data Availability/Availability of data and materials CyTOF data has been uploaded at ImmPort [https://www.immport.org/shared/home] with accession number SDY1637. All other relevant data are available in the article. Competing interests Not applicable Funding Not applicable Authors' contributions Li wrote the main manuscript text. Yu prepared all the figures. Zhu check the mistakes.All authors reviewed the manuscript. Acknowledgements Not applicable References Xiuyong D ,Shiguang Y ,Lili Z , et al. Prognostic impact of peripheral eosinophil counts in patients with diffuse large B-cell lymphoma receiving chimeric antigen receptor T-cells therapy. [J]. Cytotherapy, 2023, 25 (6): 573-577. 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Eosinophilic/T-cells Chorionic Vasculitis: A Clinicopathologic and Immunohistochemical Study of 51 Cases [J]. Pediatric and Developmental Pathology, 2011, 14 (3): 198-205. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6635903/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6635903/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84988369,"identity":"ddceb8bc-ce42-4a9c-80bc-7799d1925b4e","added_by":"auto","created_at":"2025-06-19 14:49:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156970,"visible":true,"origin":"","legend":"\u003cp\u003eExpression distribution curve\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/0aedfe2b1053b5f30532ccaa.png"},{"id":84988367,"identity":"c14531b8-828f-4fa7-b443-594da385a6c5","added_by":"auto","created_at":"2025-06-19 14:49:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70173,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of different cells in different datasets\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/113cbf6efcb9d1137da4529d.png"},{"id":84988371,"identity":"e2c3f223-278f-42ee-bc92-c1df8520a3c2","added_by":"auto","created_at":"2025-06-19 14:49:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34004,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram comparing the percentage of depleted T-cells subsets (Exhausted CD4\u003csup\u003e+\u003c/sup\u003e T-cells and Exhausted CD8\u003csup\u003e+\u003c/sup\u003e T-cells) in different datasets (JK6 and JK7)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/a08c03eb6d74a1ea2f470304.png"},{"id":84989861,"identity":"da7f6f6d-523f-4a53-84d7-fbd9ef7aa808","added_by":"auto","created_at":"2025-06-19 15:05:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45815,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms comparing the percentage of initial, effector and memory T-cells subsets in different datasets (JK6 and JK7)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/0f376edd14db0c0c89edb145.png"},{"id":84989552,"identity":"b23b08d7-dc08-4e76-911a-a055cb61d5c8","added_by":"auto","created_at":"2025-06-19 14:57:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192340,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis plot for 11 important markers\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/6243e3bf8c7ec4d0ded7eb41.png"},{"id":84989551,"identity":"83fd252a-9bb5-4242-8dc7-bf255f3a40d5","added_by":"auto","created_at":"2025-06-19 14:57:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24427,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of eosinophil ratio\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/ccda782067fb6069c0fe0a73.png"},{"id":87307755,"identity":"d8be5146-3e7b-4f3d-a137-e43c4457dd02","added_by":"auto","created_at":"2025-07-22 14:23:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6635903/v1/4b17e9f3-a027-40cd-b284-2ad1e91cae00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of eosinophils on T-cells in differently active Brain tumors","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBrain tumors are serious diseases of the nervous system, with increasing morbidity and mortality rates and a variety of types, among which gliomas are the most common and highly malignant. Conventional treatments are limited by the specific location and biological characteristics of Brain tumors, and the search for new therapies has become a hot topic. Eosinophils are derived from bone marrow hematopoietic stem cells, which play a key role in the immune system, and their role in the tumors microenvironment is of increasing interest. In a variety of solid tumors, eosinophils inhibit tumors growth and metastasis in the early stages but promote tumors progression in the later stages, and also interact with tumors-associated macrophages to regulate the immune status of the tumor microenvironment. T-cells (T lymphocyte) are central in tumor immunity and are classified into CD4⁺ helper T-cells (Th cells) and CD8⁺ cytotoxic T-cells (CTLs). Th cells can differentiate into different subpopulations, each with different functions. CD8⁺ CTL is the main effector cell for killing tumor cells. However, tumor cells can evade immune attack through multiple mechanisms. Enhancing T-cells anti-tumor activity and overcoming immune escape are key to tumor immunotherapy. Cytokines secreted by eosinophils can affect T-cells activation, proliferation and differentiation, and the chemokines they secrete can recruit T-cells, as well as interact with T-cells and regulate T-cells function through surface molecules. It is of great significance to study the effect of eosinophils on T- cells in Brain tumors. On the one hand, it can help to reveal the immune escape mechanism of Brain tumors, provide a new perspective to understand the dynamic changes of immune cells and the regulation of immune balance in the tumor microenvironment, and lay a theoretical foundation for the development of new strategies for immunotherapy. On the other hand, based on the interaction between the two, new therapeutic targets can be found, such as regulating eosinophils function to enhance the anti-tumor activity of T-cells, or blocking the immunosuppressive signals to break the immune escape of tumors, which can improve the therapeutic effect of Brain tumors.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Material\u003c/h2\u003e \u003cp\u003eThis study utilized the SDY1637 CyTOF data from the Immport database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/shared/home\u003c/span\u003e\u003cspan address=\"https://www.immport.org/shared/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only eosinophils and expression of different T-cells surface markers were included for the analysis of the study. The data sources covered different tumors models, such as GL261 and 005 in the Active group, and CT2A and Mut3 in the Inert group, which provided a rich sample base for studying the role of eosinophils on different T-cells subpopulations in Brain tumors environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cp\u003eImmune cell subpopulations were analysed by CyTOF technology to clearly define different cell subpopulations. Using this technique it was possible to precisely reveal differences in the proportions of immune cell subpopulations, such as the percentage of eosinophils in different active tumors groups and the distribution of different T-cells subpopulations in different tumors models. In addition, the cell percentages of different T-cells subpopulations in the two datasets (JK6 and JK7) were analysed, and the characteristics as well as spatial distributions of immune cells in different tumor models were explored in depth with the help of heatmap and cluster analyses, as well as the visualisation of the t-SNE downscaling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Observation indicators\u003c/h2\u003e \u003cp\u003eDifferences in immune cell composition were mainly observed in different tumor models. The enrichment of immune response pathways (e.g. antigen presentation, T-cells activation) in GL261 and 005 tumors, as well as the expression of cell cycle-related genes in CT2A and Mut3 tumors were observed by RNA sequencing analysis. Among them, the immune response pathways were significantly enriched in GL261 and 005 tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting higher immunoreactivity, while the high expression of cell cycle-related genes in CT2A and Mut3 tumors suggested their immune inert character. Meanwhile, the proportions of eosinophils in different active tumors groups (Active and Inert groups) were observed, as well as the distribution of various types of T-cells subpopulations (e.g. effector T-cells and regulatory T-cells in CD4⁺ helper T-cells, memory T-cells and depleted T-cells in CD8⁺ cytotoxic T-cells, etc.) in different distribution characteristics in different tumor models. In addition, attention was paid to the clustering distribution and spatial location relationships of immune cells in the t-SNE downscaling visualisation maps as a means of analysing the functional synergistic or inhibitory relationships between cells, as well as the correlation between eosinophils and T-cells functions, such as the association between the proportion of eosinophils and the changes in the number of different T-cells subpopulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical methods\u003c/h2\u003e \u003cp\u003eData were analysed using statistical methods. When determining differences in data between different groups, differences were determined to be statistically significant or not by setting significance levels (e.g. * for P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * for *P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** for P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For example, this method was applied to make judgements when comparing the differences in the proportions of eosinophils in the Active and Inert groups, as well as the differences in the percentages of different T-cells subpopulations in the different datasets (JK6 and JK7), so as to provide reliable data to support the conclusions of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Differences in immune cell composition in different tumors models\u003c/h2\u003e \u003cp\u003eThe immune cell composition of different tumor models varied significantly, and analysis by RNA sequencing revealed that immune response pathways (e.g. antigen presentation, T-cells activation) were significantly enriched in the GL261 and 005 tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting higher immunoreactivity, whereas the high expression of cell cycle-related genes in the CT2A and Mut3 tumors was suggestive of an immunoinert character (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCyTOF technique further revealed differences in the proportions of immune cell subpopulations, in which the proportion of eosinophils was significantly higher in the Active group (GL261: 4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8%; 005: 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1%) than in the Inert group (CT2A: 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4%; Mut3: 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that eosinophils are closely related to the tumor immunoreactivity were closely related (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the distribution of T-cells subsets, a higher proportion of CD4\u003csup\u003e+\u003c/sup\u003e effector T-cells (12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1%) and CD8\u003csup\u003e+\u003c/sup\u003e memory T-cells (8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%) were found in the Active group, whereas CD4\u003csup\u003e+\u003c/sup\u003e regulatory T-cells (Treg, 15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2%) and CD8\u003csup\u003e+\u003c/sup\u003e depletion T-cells (PD-1\u003csup\u003e+\u003c/sup\u003e, 22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1%) predominated in the Inert group. The association between immunoreactivity and T-cells functional status was further validated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of T-cells subpopulation distribution(Data analysis source \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rdcu.be/eeT6v\u003c/span\u003e\u003cspan address=\"https://rdcu.be/eeT6v\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification of immune activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEosinophil ratio(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-cells subset characteristics (percentage)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epercentage(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGL261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eImmune activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD103\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e tissue-resident memory T- cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepletion of CD8\u003csup\u003e+\u003c/sup\u003e T cells(PD-1\u003csup\u003e+\u003c/sup\u003eCD39\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e effector T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e memory T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eImmune activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassical CD8\u003csup\u003e+\u003c/sup\u003e T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepletion of CD8\u003csup\u003e+\u003c/sup\u003e T-cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e effector T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e memory T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCT2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImmune inert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepletion of CD8\u003csup\u003e+\u003c/sup\u003e T-cells(PD-1\u003csup\u003e+\u003c/sup\u003eCD39\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD39\u003csup\u003e+\u003c/sup\u003e immunosuppressive T-cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e regulatory T-cells(FoxP3\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMut3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImmune inert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e regulatory T-cells(FoxP3\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassical CD8\u003csup\u003e+\u003c/sup\u003e T-cells(CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, by further analysing the cell percentages of different T-cells subpopulations in the two datasets (JK6 and JK7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the differences in T-cells subpopulations could be understood in more detail. In terms of depleted T-cells, the percentages of \u0026lsquo;Exhausted CD4\u003csup\u003e+\u003c/sup\u003e T-cells\u0026rsquo; and \u0026lsquo;Exhausted CD8\u003csup\u003e+\u003c/sup\u003e T-cells\u0026rsquo; in the JK6 dataset were higher than those in the JK7 dataset, and the differences were highly statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the initial, effector and memory T-cells subpopulations, all subpopulations except \u0026lsquo;Memory CD4\u003csup\u003e+\u003c/sup\u003e T Cell\u0026rsquo; were also significantly different between the JK6 and JK7 data sets, and in most cases, the percentage of cells was higher in the JK6 data set than in the JK7 data set. These results further reflect the differences in the between the functional status of T-cells in the tumors microenvironment and tumors immunoreactivity distribution of T-cells subpopulations in different samples, which can help to deeply understand the relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmap and cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that Active group samples (e.g., JK1.01) had significantly high expression in CD4\u003csup\u003e+\u003c/sup\u003e initial T-cells (Z-score\u0026thinsp;=\u0026thinsp;2.5) and CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells (Z-score\u0026thinsp;=\u0026thinsp;3.0), whereas the Inert group samples (e.g., JK6.01) had expression of eosinophil-associated genes (Z-score\u0026thinsp;=\u0026thinsp;1.2) lower, suggesting significant differences in immune cell profiles across tumors models. Together, these results suggest a higher proportion of eosinophils and effector/memory T-cells in immunoreactive tumors, whereas Treg and depleted T-cells predominate in immunoinert tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation of eosinophils with T-cells function\u003c/h2\u003e \u003cp\u003eCorrelation analysis of eosinophils and T-cells function showed that in the Active group, the proportion of eosinophils was significantly and positively correlated with CD103\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e T-cells (r\u0026thinsp;=\u0026thinsp;0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells (r\u0026thinsp;=\u0026thinsp;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that eosinophils promote T-cells activation and memory phenotypic differentiation; while in the Inert group, eosinophil reduction was accompanied by an increase in PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T-cells (r=-0.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that eosinophil deficiency leads to T-cells depletion. In addition, after CT2A tumors resection, the proportion of eosinophils significantly increased from 1.3\u0026ndash;3.8% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with an increase in the proportion of CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells from 7.1\u0026ndash;14.5%, and an increase in the proportion of SiglecF\u003csup\u003e+\u003c/sup\u003e macrophages from 8.2\u0026ndash;15.6%, whereas the number of quiescent microglial cells was reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), suggesting that eosinophils after tumors removal contribute to T-cells depletion through regulation of the macrophage and T-cells functions to remodel the immune microenvironment and enhance the anti-tumor immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Synergy between eosinophils and T cells\u003c/h2\u003e \u003cp\u003eIn this study, we applied t-SNE downscaling analysis and found that there was a spatial synergistic relationship between eosinophils and T-cells in the tumors microenvironment. t-SNE results showed that the spatial distribution of eosinophils was similar to that of CD4\u003csup\u003e+\u003c/sup\u003e memory T-cells and CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells. This implies that eosinophils can regulate the function of neighbouring T-cells by secreting cytokines such as IL-5 and GM-CSF, which can promote T-cells proliferation and differentiation, and GM-CSF, which can enhance the activation and effector function of T-cells, and eosinophils can regulate the metabolism of T-cells through cell-to-cell contact. This spatial synergy was significant in immunologically active tumors, and heat map analysis also confirmed that CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells in the Active group had a Z-score value of 3.0 and were functionally active, correlating with a high proportion of eosinophils (4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8%) and spatial co-localisation, as well as a high proportion of CD4\u003csup\u003e+\u003c/sup\u003e memory T-cells (18.2%). This provides new evidence for the role of eosinophils in anti-tumor immunity and a theoretical basis for the development of eosinophil-based immunotherapeutic strategies, such as local delivery of IL-5 or GM-CSF to enhance anti-tumor immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Characterisation of depleted T-cells in immunologically inert tumors\u003c/h2\u003e \u003cp\u003eThe proportion of depleted T-cells was significantly higher in immunologically inert tumors up to 20%, with an isolated spatial distribution and suppressed function due to lack of eosinophil paracrine support. T-cells subpopulations varied across samples, with a higher proportion of depleted T-cells in JK6, a high percentage of initial, effector T cells in JK6, and a complex memory T-cells profile. Eosinophils promoted T-cells activation and proliferation in immunoreactive tumors, and the proportion was significantly lower in immunologically inert tumors (CT2A: 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4%; Mut3: 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%), resulting in T-cells entering a depleted state. Depleted T-cells highly expressed PD-1 and CD39, PD-1 inhibited T-cells activation and proliferation, and CD39 hydrolysed ATP to generate adenosine to form an immunosuppressive microenvironment. In immunoinert tumors, the proportion of PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T-cells reached 22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1%, and the expression level of CD39 was 25.7%, which was consistent with the immune escape mechanism of clinical Brain tumors, suggesting the key role of depleted T-cells in tumors progression and immunotherapy resistance, for which immunotherapeutic strategies targeting the PD-1/CD39 axis could be developed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Eosinophils as regulators of immunoreactive tumors\u003c/h2\u003e \u003cp\u003eIn immunoreactive tumors (Active group: GL261, 005), eosinophils are key regulators. It secretes cytokines such as IL-5 and GM-CSF, which significantly promote CD103\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e memory T-cells expansion (18.2%) and inhibit CD8\u003csup\u003e+\u003c/sup\u003e T-cells depletion (the proportion of PD-1\u003csup\u003e+\u003c/sup\u003e decreased to 3.7%).IL-5 activates the STAT5 signaling pathway to promote T-cells survival and memory phenotype differentiation, and GM-CSF enhances T-cells effector function through the JAK-STAT and PI3K- AKT pathways to enhance T-cells effector function. Eosinophils also regulate T cells through intercellular contacts or exosomal signaling. In contrast, in immunologically inert tumors (CT2A, Mut3), eosinophils were significantly absent (CT2A: 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4%; Mut3: 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%), and the immune microenvironment was altered and exacerbated by the predominance of Treg (15.6%) and depleted T-cells (22.4%).Treg secretion of TGF-β and IL-10 suppressed effector T-cells, and depleted T cells with high expression of PD-1 and CD39 mediated the immunosuppressive microenvironment, consistent with clinical Brain tumors immune escape mechanisms. Therefore, targeted modulation of eosinophil number and function, such as local delivery of IL-5 or GM-CSF, can help to reverse immunosuppression and provide a new strategy for GBM immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Mechanisms of immune remodelling after tumors resection\u003c/h2\u003e \u003cp\u003eEosinophil rebound (1.3%\u0026rarr;3.8%) plays a key role in the remodeling of the immune microenvironment after tumors resection. It recruits SiglecF\u003csup\u003e+\u003c/sup\u003e macrophages to synergistically activate T-cells and promote postoperative immune recovery. SiglecF\u003csup\u003e+\u003c/sup\u003e macrophages have strong antigen presentation and immunomodulatory functions, secrete pro-inflammatory factors such as IL-12 and TNF-α to enhance the activation and proliferation of T-cells, and eosinophils also act on T-cells through exosomes or cytokines to promote their differentiation to effector or memory phenotypes. Previous studies have shown that chemokines such as CCL5 released by eosinophils are important in enhancing T-cells infiltration, and in the present study, eosinophil rebound after CT2A tumors resection was associated with a significant increase in CD8\u003csup\u003e+\u003c/sup\u003e effector T-cells (7.1% \u0026rarr; 14.5%), and eosinophils also released chemokines such as CXCL9 and CXCL10 or cytokines such as IL-4 and IL-13 modulating the immune microenvironment. These provide new ideas for the development of chemokine-based postoperative immunotherapy strategies, such as local delivery of CCL5 or CXCL10 to reduce the risk of tumors recurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Clinical translation potential\u003c/h2\u003e \u003cp\u003eTargeting eosinophils provides a new strategy for Brain tumors immunotherapy. In immunologically inert tumors, local delivery of IL-5 to activate eosinophils may reverse T-cells depletion, reduce the proportion of PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T-cells from 22.4% to less than 10%, and enhance anti-tumor immune responses. CyTOF-based heat map analysis may provide a basis for patient stratification, with a high Z-score (\u0026gt;\u0026thinsp;2.5) of CD4\u003csup\u003e+\u003c/sup\u003e initial T-cells predicting a better immunotherapy response and contributing to the development of a personalised treatment plan. These reveal the potential value of eosinophils in GBM immunotherapy and point to new directions for developing therapeutic strategies based on immune microenvironmental characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eEosinophils inhibit depletion and promote memory phenotypes by regulating T- cells differentiation in immunoreactive GBM, whereas their absence in immunoinert tumors results in an immunosuppressive microenvironment. Targeting eosinophils may enhance anti-tumor immunity and provide new directions for GBM therapy. The sample size is small (only n\u0026thinsp;=\u0026thinsp;1 in the MostA ctive/Most Inert group) and needs to be expanded for validation; the molecular mechanisms of eosinophil-T-cells interactions (e.g., specific receptors) have not yet been clarified.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe undersigned authors of the manuscript entitled \"Effect of eosinophils on T-cells in differently active Brain tumors\" confirm that we consent to the publication of this work in Molecular Medicine. We irrevocably transfer all copyright ownership of the manuscript, including any tables, figures, or supplementary materials, to the publisher upon acceptance. We authorize the publisher to reproduce, distribute, display, and archive the manuscript in all forms, including print, electronic, and digital formats, and to make such versions available to the public in accordance with the journal’s policies. The authors retain the right to use the manuscript for personal or institutional purposes, such as storing it in their own or their institution’s repository, provided proper attribution to the original publication is included. We declare that the manuscript is original, has not been published elsewhere, and is not under consideration for publication in another journal. All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability/Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCyTOF data has been uploaded at ImmPort [https://www.immport.org/shared/home] with accession number SDY1637. All other relevant data are available in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi wrote the main manuscript text. Yu prepared all the figures. Zhu check the mistakes.All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXiuyong D ,Shiguang Y ,Lili Z , et al. Prognostic impact of peripheral eosinophil counts in patients with diffuse large B-cell lymphoma receiving chimeric antigen receptor T-cells therapy. [J]. Cytotherapy, 2023, 25 (6): 573-577.\u003c/li\u003e\n\u003cli\u003eRafael C ,M I S ,Natalio G , et al. Corrigendum: Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(\u003csup\u003e+\u003c/sup\u003e) T cells. [J]. Nature immunology, 2016, 17 (2): 214.\u003c/li\u003e\n\u003cli\u003eSamah K ,Feryal I ,Ilham B , et al. A Challenging Case of Gamma Delta T-cells Lymphoma with Precursor T-cells and Marked Eosinophilia: A Case Report. [J]. Case reports in oncology, 2020, 13 (3): 1520-1529.\u003c/li\u003e\n\u003cli\u003eMagda Z ,G. G L ,Elena S , et al. T-cells Lymphoblastic Lymphoma Arising in the Setting of Myeloid/Lymphoid Neoplasms with Eosinophilia: LMO2 Immunohistochemistry as a Potentially Useful Diagnostic Marker [J]. Cancers, 2021, 13 (12): 3102-3102.\u003c/li\u003e\n\u003cli\u003eC M B ,M G B ,P Z M , et al. Intratumor childhood vaccine-specific CD4\u003csup\u003e+\u003c/sup\u003e T-cells recall coordinates antitumor CD8\u003csup\u003e+\u003c/sup\u003e T cells and eosinophils. [J]. Journal for immunotherapy of cancer, 2023, 11 (4):\u003c/li\u003e\n\u003cli\u003eDaniel D . T-cells chronic active EBV diagnosed in a 65-year-old male presenting as idiopathic hypereosinophilic syndrome [J]. Clinical Immunology, 2023, 250 (S):\u003c/li\u003e\n\u003cli\u003eSasan G ,Nima R . Eosinophils in the tumor microenvironment: implications for cancer immunotherapy. [J]. Journal of translational medicine, 2023, 21 (1): 551-551.\u003c/li\u003e\n\u003cli\u003eG G L ,Stefano A ,Francesco M , et al. Concomitant myeloproliferative neoplasm with eosinophilia, B and T cell lymphoblastic lymphoma/leukemia and mast cell proliferation driven by ZMYM2::FGFR1 rearrangement. [J]. American journal of hematology, 2023, 98 (12): 1959-1962.\u003c/li\u003e\n\u003cli\u003eLucy B . T cell-eosinophil collaboration. [J]. Nature reviews. Immunology, 2022, 23 (2): 72-72.\u003c/li\u003e\n\u003cli\u003eJaoude A E ,Nsouli T ,Bellanti J . GLEICH SYNDROME, RARE CASE OF EPISODIC ANGIOEDEMA WITH SEVERE HYPEREOSINOPHILIA AND ASSOCIATED ABNORMAL T-CELLS PHENOTYPE [J]. Annals of Allergy, Asthma \u0026amp; Immunology, 2023, 131 (5S1): S130-S131.\u003c/li\u003e\n\u003cli\u003eJ\u0026eacute;r\u0026eacute;my S ,Julie G ,Yann G , et al. T cell phenotype and lack of eosinophilia are not uncommon in extramedullary myeloid/lymphoid neoplasms with ETV6::FLT3 fusion: a case report and review of the literature. [J]. Virchows Archiv : an international journal of pathology, 2023, 484 (5): 853-857.\u003c/li\u003e\n\u003cli\u003eBeliak N ,Kutukova S ,Manikhas G , et al. Correlation of intratumoral CD8(\u003csup\u003e+\u003c/sup\u003e) T-cells, neutrophils and eosinophils frequency with morphological characteristics and clinical outcome of gastrointestinal adenocarcinomas [J]. Annals of Oncology, 2016, 27 (Supl.6): vi25-vi25.\u003c/li\u003e\n\u003cli\u003eEosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(\u003csup\u003e+\u003c/sup\u003e) T cells (vol 16, pg 609, 2015) [J]. Nature immunology, 2016, 17 (2): 214-214.\u003c/li\u003e\n\u003cli\u003eEvelina S ,Francesca S ,Ione T , et al. Discordant Eosinophilic/T-cells Chorionic Vasculitis in a Dichorionic Diamniotic Placenta. [J]. International journal of molecular sciences, 2023, 24 (11):\u003c/li\u003e\n\u003cli\u003eStefanie B ,Michel H ,JeanLouis D . [Eosinophilic/T-cells chorionic vasculitis: A rare and particular inflammatory disorder of the placenta]. [J]. Annales de pathologie, 2023,\u003c/li\u003e\n\u003cli\u003eC G T ,P Y W ,R R A . Eosinophilic/ T cell chorionic vasculitis. [J]. The Malaysian journal of pathology, 2023, 45 (1): 145-146.\u003c/li\u003e\n\u003cli\u003eValentina G ,Francesca P ,Cristiano C , et al. Preliminary Assessment of Tumor-Associated Tissue Eosinophilia (TATE) in Canine Mast Cell Tumors: Prevalence and Prognostic Relevance and Its Association with Neoangiogenesis [J]. Animals, 2023, 13 (2): 283-283.\u003c/li\u003e\n\u003cli\u003eJason G . Available and emerging therapies for bona fide advanced systemic mastocytosis and primary eosinophilic neoplasms. [J]. Hematology. American Society of Hematology. Education Program, 2022, 2022 (1): 34-46.\u003c/li\u003e\n\u003cli\u003eHarpreet V ,Shailja R ,Anshu A , et al. Imatinib responsive erythrocytosis in a patient with FIP1L1::PDGFRA rearranged myeloid neoplasm with hypereosinophilia \u0026ndash; Another manifestation of a stem cell neoplasm [J]. Leukemia Research, 2022, 121 106922-106922.\u003c/li\u003e\n\u003cli\u003eSamah K ,Feryal I ,Ilham B , et al. A Challenging Case of Gamma Delta T-cells Lymphoma with Precursor T-cells and Marked Eosinophilia: A Case Report. [J]. Case reports in oncology, 2020, 13 (3): 1520-1529.\u003c/li\u003e\n\u003cli\u003eNohr W E ,Wright R J . Discordance of Twin Placentas for Multifocal Eosinophilic/T-cells Chorionic Vasculitis [J]. Pediatric and Developmental Pathology, 2019, 22 (1): 40-44.\u003c/li\u003e\n\u003cli\u003eKatzman J P ,Li L ,Wang N . Identification of Fetal Inflammatory Cells in Eosinophilic/T-cells Chorionic Vasculitis Using Fluorescent in Situ Hybridization [J]. Pediatric and Developmental Pathology, 2015, 18 (4): 305-309.\u003c/li\u003e\n\u003cli\u003eBradley C ,Stephen H ,Kenneth W , et al. Eosinophilic/T-cells Chorionic Vasculitis: Histological and Clinical Correlations. [J]. Fetal and pediatric pathology, 2015, 34 (2): 73-9.\u003c/li\u003e\n\u003cli\u003eVillanueva T M . Eosinophils \u0026mdash; T cells\u0026apos; little helpers [J]. Nature Reviews Cancer, 2015, 15 (6): 320-320.\u003c/li\u003e\n\u003cli\u003eJacques M S ,Qureshi F ,Kim J C , et al. Eosinophilic/T-cells Chorionic Vasculitis: A Clinicopathologic and Immunohistochemical Study of 51 Cases [J]. Pediatric and Developmental Pathology, 2011, 14 (3): 198-205.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Eosinophils, T-cells, Brain tumors","lastPublishedDoi":"10.21203/rs.3.rs-6635903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6635903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the effect of eosinophils on T-cells in various active Brain tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy analyzing the SDY1637 CyTOF data in Immport database, we observed the differences in immune cell composition and the correlation between eosinophils and T-cells function in different tumor models by applying CyTOF technology, heatmap and clustering analysis, and t-SNE downscaling visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of eosinophils and effector/memory T-cells was higher in immunoreactive Brain tumors, there was spatial synergy between them, and eosinophils promoted T-cells activation and memory phenotype differentiation; regulatory T-cells and depleted T-cells dominated in immunoinert Brain tumors, and eosinophils deficiency led to T-cells depletion. After tumor resection, eosinophils rebounded and remodeled the immune microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEosinophils have a significant impact on T-cells function in different active Brain tumors, targeting eosinophils is expected to enhance anti-tumor immunity and provide a new direction for Brain tumors therapy, and the molecular mechanism of the interaction between the two remains to be further investigated.\u003c/p\u003e","manuscriptTitle":"Effect of eosinophils on T-cells in differently active Brain tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 14:49:22","doi":"10.21203/rs.3.rs-6635903/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89b43b2f-7a76-458d-a419-1698f151ee21","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50266588,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":50266589,"name":"Biological sciences/Cancer/Tumour immunology"}],"tags":[],"updatedAt":"2025-07-22T14:23:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-19 14:49:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6635903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6635903","identity":"rs-6635903","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}