Neutrophil progenitor mobilization is a distinct feature of STEMI and predicts survival

Neutrophil progenitor mobilization is a distinct feature of STEMI and predicts survival | 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 Brief Communication Neutrophil progenitor mobilization is a distinct feature of STEMI and predicts survival Mathis Richter, Jessica von Göwels*, Maximilian Fähndrich*, Christian Lipgens Fernandez*, and 30 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6474581/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted You are reading this latest preprint version Show more versions Abstract Inflammatory cardiovascular diseases are the leading cause of death worldwide. Neutrophils, key drivers during inflammation, have consistently been associated with adverse outcomes and mortality in cardiovascular pathologies. Given the uniquely high plasticity and turnover of neutrophils, we profiled the appearance of distinct neutrophil maturation stages as sensitive and specific biomarkers in patients with ST-elevation myocardial infarction (STEMI), heart failure and stroke. Our data reveal a neutrophilia driven by mobilization of immature neutrophils in all groups; however, STEMI patients exhibited selective appearance of CD16 neg CD10 neg preNeus, the final mitotic neutrophil progenitor. PreNeus predicted 30-day mortality better than established biomarkers and were identifiable as immature granulocytes in blood counter analysis, hence enabling identification of high-risk STEMI patients upon hospital admission to guide tailored intervention. Health sciences/Biomarkers/Prognostic markers Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction Figures Figure 1 Figure 2 Main Over decades, neutrophils were considered bystanders in cardiovascular inflammation. In the last decade, however, this paradigm shifted as numerous studies unveiled causal roles for neutrophils across several inflammatory cardiovascular pathologies 1 , 2 . Yet, such analyses typically consider the global neutrophil population and do not discriminate between neutrophil maturation and activation stages. Neutrophils exhibit the highest cellular turnover of all cells in the human body 3 . They develop in the bone marrow from specialized stem and progenitor cells, which are collectively referred to as the mitotic pool. Neutrophils develop in a stepwise manner from granulocyte-monocyte progenitors (GMPs). The granulopoietic trajectory continues with proNeu1 which transition to proNeu2 and further to proliferative preNeus, which ultimately give rise to post-mitotic immature and mature neutrophils 4 , 5 . This maturation process is a highly-organized process characterized by the stepwise acquisition of maturation markers (e.g., CD16 and CD10) and loss of retention markers (e.g., CXCR4, CD49d). The circulating neutrophil compartement in steady-state is almost exclusively comprised of mature cells; yet, in situations of inflammation and subsequent emergency granulopoiesis 6 , immature cells are mobilized to the blood stream. This phenomenon, historically referred to as "left shift", reflects the increased release of different stages of neutrophil maturation from the bone marrow in order to meet the elevated peripheral demand. 7 We here hypothesized that the appearance of neutrophil maturation stages in the blood stream may in fact resemble a biosensor of disease severity that serves as a predictive biomarker in cardiovascular pathologies. Using high dimensional spectral flow cytometry, we profiled the neutrophil maturation status in patients with heart failure (HF), ischemic stroke and ST-elevation myocardial infarction (STEMI) (Fig. 1 A). In agreement with previous reports, patients in all three pathologies exhibit signs of emergency granulopoiesis including neutrophilia and the appearance of immature neutrophils (% of CD45 + cells) (Fig. 1 B, Suppl. Figure 1 ). Yet, full spectrum flow cytometry permits deep profiling of neutrophil maturation stages. Based on the expression of CD10 and CD16, we divided neutrophil maturation in 11 bins of continuous neutrophil maturation revealing a progressive and dynamic remodelling of the neutrophil surface composition along maturation (Fig. 1 C). Interestingly, the most immature cluster exhibited overt expression of the retention molecules CXCR4 and CD49d and incorporated the thymidine analogue 5-Ethynyl-2'-deoxyuridine ex vivo (EdU), thus reflecting the last proliferating neutrophil progenitor, i.e. the preNeu (Fig. 1 D). In agreement herewith, fluorescence-activated cell sorting (FACS) followed by Giemsa staining revealed a segmented nucleus in CD16 high CD10 high mature neutrophils, a banded nucleus in CD16 int CD10 low immature neutrophils and a round nucleus in CD16 neg CD10 neg preNeus (Fig. 1 E). Quantification of neutrophil maturation bins in different disease cohorts revealed that preNeus were present in STEMI patients but virtually absent in blood from HF and stroke patients (Fig. 1 F /G ), suggesting their specific and exacerbated mobilization in this particular disease setting. Having identified preNeu mobilization as a specific feature of myocardial ischemia, we systematically assessed the factors potentially involved in this process. To this end, we performed Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) analysis of circulating neutrophils allowing to visualize neutrophil maturation across its entire spectrum (Fig. 1 H). Interestingly, patients admitted for acute chest pain (ACP) (with no signs of myocardial ischemia) showed only low levels of preNeus compared to STEMI patients, indicating that acute pain and associated stress responses may not be causally involved in preNeu mobilization. Analysis of the preNeu counts 3–6 months after the initial STEMI revealed that preNeu levels returned to normal implying that their mobilization is indeed just a transient state. Subdivision of STEMI patients based on Killip classification showed increased preNeu mobilization in STEMI patients with ischemic heart failure. Finally, analysis of preNeu levels based on 30-day survival after STEMI revealed significantly higher preNeus in non-survivors. In line with this finding, receiver operating curve (ROC) analysis of preNeus as a predictor of survival yielded an area under the curve (AUC) of 0.78 in this exploratory STEMI cohort ( Suppl. Figure 2 ). Taken together, our findings reveal a strong association of preNeu mobilization with clinical severity and outcome after STEMI. The use of complex spectral flow cytometry to identify and quantify circulating preNeus in an emergency setting represents a significant challenge in routine clinical practice. Consequently, correlation analysis of neutrophil maturation with blood parameters gathered in the clinic in the vicinity of the intervention led to the identification of the parameter "immature granulocytes (IGs)" available from differential blood cell analysis as a potential surrogate marker ( Suppl . Figure 3). Clinical blood counter tests were conducted on the majority of STEMI patients (30/37). Indeed, preNeus identified by spectral flow cytometry analysis correlated with the IGs from the automated blood counter analysis (Fig. 2 A). To further support the notion that preNeus identified by flow cytometry are indeed IGs in the clinical blood counts, we isolated mature neutrophils, immature neutrophils, and preNeus by FACS based on their CD66b, CD10, and CD16 expression profiles (Fig. 2 B, Suppl. Figure 4A ). Notably, only sorted preNeus but not mature and immature neutrophils were found in the IG gate of the automated blood counter (Fig. 2 B, Suppl. Figure 4B ). Clinical blood counters identify IGs based on scatter characteristics and RNA content. In support of our analyses, RNA content decreases in a step-wise manner during maturation from preNeus to mature neutrophils ( Suppl. Figure 4C ). Of note, we further sorted eosinophils from these samples to exclude a potential eosinophil contamination by both Giemsa staining and blood counter analysis ( Suppl. Figure 4D ). Having identified a readily accessible biomarker for approximating circulating preNeus, we proceeded to ascertain its capacity to predict outcome after STEMI. In accordance with the results of our flow cytometric analyses, the levels of IGs were markedly elevated in non-survivors. Indeed, the AUC under the ROC for IGs exceeded that of all other cell types and plasma markers, including a range of established biomarkers such as neutrophil-lymphocyte ratio (NLR), creatine kinase (CK), CK-MB, and lactate dehydrogynase (LDH) (Fig. 2 C /D ). Given the limited size of our cohort, we performed retrospective analyses of STEMI patients admitted to the University Hospital Münster since 2019 and receiving a differential blood cell count on admission (n = 376). Among these, 62 died in the first 30 days after STEMI. Corroborating the findings described above, patients dying within the first 30 days after STEMI exhibited elevated neutrophil and IG counts (Fig. 2 E). Notably, IGs remained the most effective blood biomarker for prediction of mortality (AUC = 0.79, Fig. 2 F). After Youden index-based IG cut-off identification (Fig. 2 G) we analysed the survival of IG high patients (≥ 0.11 x 10 6 / mL) vs IG low patients (< 0.11 x 10 6 / mL). Strikingly, IG high patients had a more than 5-times higher risk of dying (log-rank Hazard Ratio (HR) = 5.22), while this risk was less prevalent in patients stratified by total neutrophil counts (HR = 3.13), LDH (HR = 3.27) or CK-MB (HR = 2.67) (Fig. 2 H /I ). Furthermore, we validated these findings in a larger NCT-listed, prospective ongoing cohort of STEMI patients receiving contemporary guideline-directed therapy with complete revascularization 8 including 491 patients of which 50 died within 30 days. In accordance with the findings from the exploratory and derivation cohorts, IGs were elevated in non-survivors and represented the best-performing biomarker to separate survivors from non-survivors (AUC = 0.81, Fig. 2 J-L). Furthermore, using the pre-identified cut-off of 0.11 x 10 6 IGs / mL we found that in this cohort IG high patients had an over 6-times higher risk of death (HR = 6.81). IGs in this cohort were also superior to neutrophils (HR = 4.25), LDH (HR = 3.50), and CK-MB (HR = 2.43) at identifying high-risk patients (Fig. 2 M /N ). In a selected subgroup of the validation cohort, IGs were also analysed on day 1, day 5, and, in survivors, at 6 months after the STEMI. In fact, IG number remained correlated with 30-day mortality even when determined at day 1 or day 5 post AMI ( Suppl. Figure 5 ). Inflammation is a key pathogenic driver ensuing upon myocardial ischemia, but immune states and subsequent clinical implications remain poorly defined. Multiomic factor analysis has recently revealed multifaceted rewiring of the immune landscape upon cardiac ischemia 9 . Yet, the complexity of this analytical approach prohibits its application in an acute clinical setting. Of note, the assessment of neutrophils, whose omics features are not readily accessible by routine transcriptomic workflows, were not assessed in this study. Our dedicated neutrophil-centric analyses indicated that blood from patients with stroke, heart failure and STEMI exhibits non-discriminating features of emergency granulopoiesis including neutrophilia and the appearance of immature neutrophils in the blood which have been linked to adverse outcomes across various conditions including cardiovascular inflammation and infections due to their heightened proinflammatory activity including reactive oxygen species (ROS) production and neutrophil extracellular trap (NET) release 10 , 11 . Our data further indicate the specific mobilization of preNeus in myocardial ischemia. Given their low frequency in blood and their quick disappearance form the circulation, it is unlikely that these cells play a key pathogenic role during the acute insult. Yet, it is intriguing to speculate about potential mechanisms of their release. In our hands, preNeus did not correlate with levels of CK-MB and LDH upon admission suggesting that infarct size may not be the main driver of preNeu mobilization. In addition, patients with chest pain of extracardial origin did not display preNeus in blood suggesting that pain sensation or increased sympathetic activation are not major contributors. Thus, it appears likely that the discharge of preNeus to the blood stream is a measure of an overt, misdirected systemic inflammatory response that clinically manifests as increased mortality. In summary, our findings indicate that the presence of preNeus is a characteristic feature of STEMI pathophysiology and identifies a severe patient subgroup associated to decreased 30-day survival. Our data further show that immature granulocytes, readily accessible by automated blood counters in routine clinical laboratories, in fact comprised preNeus and represent a biomarker that is a measure of the inflammatory response after coronary occlusion and outperforms traditional biomarkers in predicting survival after MI. Methods Ethics of flow cytometry patient groups Informed consent was obtained from healthy donors and patients with the approval of the Ethics Committee of University of Münster (study numbers 2021-424-f-S, 2021-532-f-S, 2023-348-f-S, 2023-489-f-S and 2024-253-f-S). We collected EDTA blood between 2022–2024 at the University Hospital Münster from n = 37 STEMI patients before revascularization (age (median (IQR)): 59 (50.75–69); male (%): 33 (89%)), 29 patients with acute chest pain without troponin dynamics in the emergency department (age (median (IQR)): 54.5 (47.75–67.25); male (%): 19 (65%)) to assess the potential influence of factors not exclusively associated with MI (e.g., fear of death, stress, pain), 21 ischemic stroke patients within the first 24 h after onset of symptoms (age (median (IQR)): 62 (53.5–82.5); male (%): 15 (71%)), 67 patients with heart failure with reduced ejection fraction (HFrEF; LVEF < 40%; age (median (IQR)): 65.5 (59.5–75); male (%): 55 (82%)) and 59 healthy controls (age (median (IQR)): 58 (42–63.5); male (%): 37 (63%)). For all cohorts represented in Fig. 1 , patients with anaemia, acute infectious diseases, acute inflammatory diseases or chronic inflammatory diseases; kidney, liver, haematological or coagulation disorders; malignancies; recent trauma; surgery in the last 6 months; severe bleeding requiring a blood transfusion; hormonal treatment or taking anti-inflammatory drugs, genetic disorders or psychiatric abnormalities; pregnancy and lactation were excluded. Blood was processed immediately afterwards to preserve the acute neutrophil phenotype. Details on derivation STEMI cohort Anonymised patient information including blood counter analysis, clinical chemistry and survival from STEMI patients at the University Hospital Münster between 2019 and 2024 were gathered using a data bank query at the Medical Data Integration Center (MeDIC) of the University Münster with the approval of the Ethics Committee of University of Münster (study number 2024-185-f-S). Patients were identified as STEMI patients based on their main diagnosis ICD-10 code (I21.0 – I21.3). Details on validation STEMI cohort (SYSTEMI) All patients were enrolled in the systemic organ communication in STEMI (SYSTEMI) study (NCT03539133), an open-end prospective cohort study that collects data from STEMI and high-risk non-STEMI patients treated at the University Hospital Duesseldorf. 7 The study has been approved by the ethics committees of the Heinrich-Heine-University in Duesseldorf, Germany (#5961R), and complies with the Declaration of Helsinki. Informed consent was obtained from all enrolled patients included. Blood used for automated differential blood count was drawn following the AMI diagnosis at hospital admission before starting coronary angiography and revascularization. In a subcohort of patients in that differential blood counts were available throughout the entire time course following AMI, IG dynamics at day 1, day 5 and 6-month follow-up were analysed. Detailed information about the biosampling protocol was previously published. 7 STEMI patients admitted to the hospital between 2019 and 2023 of which automated differential blood counts including immature granulocyte count and all other parameters were available were included in the study. Following PCI, all patients received standard medication and were treated according to the European guidelines 12 . Spectral flow cytometry For flow cytometric analysis of leukocytes, 200µl EDTA blood was incubated with red blood cell lysis buffer (Biolegend) for 20 minutes on ice for erythrocyte lysis. After washing, cells were centrifuged and resuspended in HANKs buffer (HBSS w/o Mg 2+ and Ca 2+ , 0.06% BSA, 0.3mM EDTA) and unwanted FcR-involved staining blocked using Human TruStain FcX (Biolegend). Afterwards, cells were incubated with a combination of the following fluorophore-conjugated antibodies for 20 min on ice in Cell Staining buffer (Biolegend) prior to measurement. Antigen Fluorophore Clone Vendor Catalogue# CD45 Alexa Fluor 700 HI30 Biolegend 304024 CD8 APC-H7 SK1 BD Bioscience 560179 CD10 BUV395 HI10a BD Bioscience 563871 CD16 BUV496 3G8 BD Bioscience 612944 CD34 BUV563 581 BD Bioscience 748389 CD114 BUV615 LMM741 BD Bioscience 751179 HLA-DR BUV661 G46-6 BD Bioscience 612980 CD15 BUV805 W6D3 BD Bioscience 742057 CD117 BV421 104D2 Biolegend 313216 CD54 BV480 HA58 BD Bioscience 746638 CD45RA BV570 HI100 Biolegend 304132 CD14 BV605 M5E2 Biolegend 301834 CD182 BV650 6C6 BD Bioscience 744198 CD79b BV711 CB3-1 BD Bioscience 743229 CD184 BV785 12G5 Biolegend 306530 CD66b FITC G10F5 Biolegend 305104 CD4 Pacific Blue SK3 Biolegend 344620 CD62L PE DREG-56 Biolegend 304806 CD19 PE- Dazzle594 HIB19 Biolegend 302252 CD101 PE-Vio770 REA954 Miltenyi Biotec 130-115-832 CD49d PE/Cyanine5 REA954 Biolegend 304306 CD11b PerCP-Cy5.5 ICRF44 Biolegend 301328 CD3 Spark Blue 550 SK7 Biolegend 344852 CD182 APC 5E8 Biolegend 320710 CD184 APC Cy7 12G5 Biolegend 306528 CD97 BUV563 VIM3b BD Biosciences 748366 CD11a PE-Dazzle594 HI111 Biolegend 301231 CD66b Pacific Blue G10F5 Biolegend 305112 Data acquisition was performed on a 5L-Cytek® Aurora (Cytek Biosciences) and data were analysed using FlowJo v10.10.0 (BD Bioscience), Rstudio and GraphPad Prism 10 (GraphPad Software, USA). Doublets were excluded using forward and side scatter characteristics and dead cells using DAPI. Neutrophils were identified as CD66b + SSC high CD49d low cells, eosinophils as CD66b + SSC high CD49d high cells, B cells as CD66b − CD19 + CD3 − cells, T cells as CD66b − CD19 − CD3 + cells and further divided into CD4 + and CD8 + T cells while monocytes were defined as CD66b − CD19 − CD3 − HLA-DR high CD101 high cells in FlowJo (Suppl. Figure 1A). UMAP dimensional reduction was then performed in FlowJo and visualized using R. Cytokine assays For the isolation of plasma, EDTA blood was centrifuged at 2,000 × g for 15 min at 4°C. Afterward, the supernatant was carefully removed and cryopreserved at − 80°C. For analysis of cytokines and inflammatory proteins LEGENDplex assays (740809 and 740590, Biolegend) were performed as described by the manufacturer and data acquisition performed on a CytoFLEX S flow cytometer (4L, Beckman Coulter). Clinical blood test The clinical blood tests were performed according to local standard of care within the first 24h of hospitalization. We involved the following parameters: CK, CK-MB, LDH, neutrophils, lymphocytes, monocytes and immature granulocytes. Differential blood count analyses were performed in the central clinical laboratories using a Sysmex XN9000 (University Hospital Düsseldorf), XN-9100 or XN-1000 (University Hospital Münster) (all SYSMEX EUROPE SE). Cell sorting of granulocyte subsets and blood counter analysis EDTA blood was lysed and FcR-blocked before cells were stained using the following cleavable antibodies: anti-CD66b (FITC, Miltenyi Biotec, 1:50), anti-CD10 (APC, Miltenyi Biotec, 1:50) and anti-CD16 (PerCP, Miltenyi Biotec, 1:50). After mature and immature neutrophils, preNeus and eosinophils were sorted using an FACS Aria III (BD), antibodies were cleaved from the sorted cells and the input using REAlease reagent (Miltenyi Biotec) to not interfere with blood counter analysis. Sorted cells were then measured in a Sysmex XN-1000 (SYSMEX EUROPE SE). Since the system is not designed to identify and annotate isolated cell types, we extracted the .fcs files from the machine and performed the cell type identification using sideward scatter characteristics and side fluorescence signal in FlowJo. For this, we placed the gates for neutrophils, monocytes, lymphocytes, eosinophils and immature granulocytes based on the whole blood and the automatic annotation of these samples in the machine. Giemsa staining Flow-sorted cell populations were spun onto object slides using a Cellspin III centrifuge (Thermac, 5 min, 270 x g). Cells were fixed in methanol for 7 min and incubated (45 min) with Giemsa stain (Sigma-Aldrich) before imaging. Proliferation assay EDTA blood was lysed and then incubated with Ethynyl-2'-deoxyuridine (EdU, Baseclick, 10 µM) in RPMI medium (Gibco, with 2% FCS) for 1h at 37° C and 5%CO 2 . Cells were then stained using conjugated antibodies before EdU incorporation was detected according to manufacturer instructions (Baseclick) and acquired on a flow cytometer. Statistical analyses Statistical analysis was performed using GraphPad Prism 10 (GraphPad Software) and R. The indicated statistical tests for continuous variables were performed dependent on normal distribution and equality of standard deviation. Statistical tests indicated in the figure legend were used. Data is displayed as boxplots, mean \(\:\pm\:\) SEM, or mean \(\:\pm\:\) 95% confidence interval as indicated in the figure legends. Declarations Data availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. Declaration of AI-assisted technologies in the writing process During the preparation of this manuscript, the authors used ChatGTP-4o mini (OpenAI) and DeepL Write (DeepL) to improve English language and readability. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Acknowledgements We want to thank Lina Vöcking for technical help, Thorsten König for help with cell sorting and the patients and their families for agreeing to participate in this study. Sources of Funding O.S. receives support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (CRC TRR332 projects A2 and Z1, CRC1123 project A6, CRU342 project 1, project 502158695, SO876/16-1), the Leducq Foundation, Novo Nordisk, the EU (PRAETORIAN Doctoral Network), the Else-Kröner Fresenius Stiftung, and the IZKF and the IMF of the University of Münster. C.S.R. is supported by the DFG (CRC TRR332 project A1, CRC1123 project A6) and the IZKF of the University of Münster. J.R receives funding from the DFG (CRU342 project Z, RO4537/5-2). This study was also supported by the DFG - Grant No. 236177352-CRC1116; projects B06, B09 and B12 to M.K., C.J., N.G., and Grant No. 397484323-CRC/TRR259; projects A05 to N.G. This work was supported by the Research Commission of the Medical Faculty of the Heinrich-Heine-University to A.L. (No. 2021-10). Disclosures: O.S. advises Novo Nordisk and Roche and receives funding from Novo Nordisk. References Silvestre-Roig, C., Braster, Q., Ortega-Gomez, A. & Soehnlein, O. Neutrophils as regulators of cardiovascular inflammation. Nature Reviews Cardiology 17 , 327-340 (2020). https://doi.org:10.1038/s41569-019-0326-7 Luo, J., Thomassen, J. Q., Nordestgaard, B. G., Tybjærg-Hansen, A. & Frikke-Schmidt, R. Neutrophil counts and cardiovascular disease. European Heart Journal 44 , 4953-4964 (2023). https://doi.org:10.1093/eurheartj/ehad649 Sender, R. & Milo, R. The distribution of cellular turnover in the human body. Nature Medicine 27 , 45-48 (2021). https://doi.org:10.1038/s41591-020-01182-9 Evrard, M. et al. Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions. Immunity 48 , 364-379.e368 (2018). https://doi.org:https://doi.org/10.1016/j.immuni.2018.02.002 Kwok, I. et al. Combinatorial Single-Cell Analyses of Granulocyte-Monocyte Progenitor Heterogeneity Reveals an Early Uni-potent Neutrophil Progenitor. Immunity 53 , 303-318.e305 (2020). https://doi.org:10.1016/j.immuni.2020.06.005 Swann, J. W., Olson, O. C. & Passegué, E. Made to order: emergency myelopoiesis and demand-adapted innate immune cell production. Nature Reviews Immunology 24 , 596-613 (2024). https://doi.org:10.1038/s41577-024-00998-7 Palomino-Segura, M., Sicilia, J., Ballesteros, I. & Hidalgo, A. Strategies of neutrophil diversification. Nature Immunology 24 , 575-584 (2023). https://doi.org:10.1038/s41590-023-01452-x Bönner, F. et al. SYSTEMI - systemic organ communication in STEMI: design and rationale of a cohort study of patients with ST-segment elevation myocardial infarction. BMC Cardiovascular Disorders 23 , 232 (2023). https://doi.org:10.1186/s12872-023-03210-1 Pekayvaz, K. et al. Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes. Nature Medicine 30 , 1696-1710 (2024). https://doi.org:10.1038/s41591-024-02953-4 Fraccarollo, D. et al. Expansion of CD10neg neutrophils and CD14+HLA-DRneg/low monocytes driving proinflammatory responses in patients with acute myocardial infarction. eLife 10 , e66808 (2021). https://doi.org:10.7554/eLife.66808 Schulte-Schrepping, J. et al. Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment. Cell 182 , 1419-1440.e1423 (2020). https://doi.org:10.1016/j.cell.2020.08.001 Byrne, R. A. et al. 2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC). European Heart Journal 44 , 3720-3826 (2023). https://doi.org:10.1093/eurheartj/ehad191 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6474581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2025-04-23 05:35:50","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cardiovascular-research","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcardiovascres","sideBox":"Learn more about [Nature Cardiovascular Research](https://www.nature.com/natcardiovascres/)","snPcode":"","submissionUrl":"https://mts-natcardiovascres.nature.com/cgi-bin/main.plex","title":"Nature Cardiovascular 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Münster","correspondingAuthor":false,"prefix":"","firstName":"Jan-Niklas","middleName":"","lastName":"Heming","suffix":""},{"id":446689143,"identity":"893fe1d0-c7f2-4f7a-90a8-f4fa01a32c7e","order_by":12,"name":"Hanna Aleth","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Aleth","suffix":""},{"id":446689144,"identity":"edd93000-b4a4-4ed4-846a-118d9ee58de9","order_by":13,"name":"Nicole Rübsamen","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Rübsamen","suffix":""},{"id":446689145,"identity":"c1dbe865-8b24-40f7-b4f5-893c9fbd2b52","order_by":14,"name":"Tobias Radecke","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Radecke","suffix":""},{"id":446689146,"identity":"30a4a0cb-b246-49de-b23c-4249749d64de","order_by":15,"name":"Frank Rosenbauer","email":"","orcid":"https://orcid.org/0000-0001-7977-9421","institution":"University of Muenster","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Rosenbauer","suffix":""},{"id":446689147,"identity":"698ed752-bbdc-4c45-b9c6-4876b2e862fb","order_by":16,"name":"Albert Sickmann","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Sickmann","suffix":""},{"id":446689148,"identity":"b7e51812-4ae4-4326-9760-e700a1ece18f","order_by":17,"name":"André Karch","email":"","orcid":"https://orcid.org/0000-0003-3014-8543","institution":"","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"","lastName":"Karch","suffix":""},{"id":446689149,"identity":"513a2b46-d2e3-426a-b22d-f77a237f6818","order_by":18,"name":"Alexander Zarbock","email":"","orcid":"https://orcid.org/0000-0002-2124-1714","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Zarbock","suffix":""},{"id":446689150,"identity":"95cce147-aa8a-4a1b-b2fa-7e0055beb2c1","order_by":19,"name":"Jens Minnerup","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"","lastName":"Minnerup","suffix":""},{"id":446689151,"identity":"6f3d130f-339a-4f1d-9671-ac153ee57960","order_by":20,"name":"Jürgen Sindermann","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Jürgen","middleName":"","lastName":"Sindermann","suffix":""},{"id":446689152,"identity":"560fc775-053b-4ae8-91e0-654b91af86b8","order_by":21,"name":"Alexander Bender","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Bender","suffix":""},{"id":446689153,"identity":"9c980aca-37f6-41ed-9da3-7fcab3855731","order_by":22,"name":"Jan Rossaint","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Rossaint","suffix":""},{"id":446689154,"identity":"ad34656e-a6ef-48cc-8e7b-293aa404c73e","order_by":23,"name":"Steffen Ormanns","email":"","orcid":"","institution":"Medical University of Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"","lastName":"Ormanns","suffix":""},{"id":446689155,"identity":"b42aeb41-707c-423e-9305-5428158195eb","order_by":24,"name":"Matthias Gunzer","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Gunzer","suffix":""},{"id":446689156,"identity":"83ea48ef-b51d-4b35-9e77-5880f85c7e72","order_by":25,"name":"Holger Reinecke","email":"","orcid":"","institution":"University Hospital Münster","correspondingAuthor":false,"prefix":"","firstName":"Holger","middleName":"","lastName":"Reinecke","suffix":""},{"id":446689157,"identity":"ff4f8c5c-55aa-43c3-bf97-50c0150756a8","order_by":26,"name":"Len Makowski","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Len","middleName":"","lastName":"Makowski","suffix":""},{"id":446689158,"identity":"1ead486b-d813-48d0-8d82-af458e2f3780","order_by":27,"name":"Christian Jung","email":"","orcid":"https://orcid.org/0000-0001-8325-250X","institution":"Heinrich-Heine-University","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Jung","suffix":""},{"id":446689159,"identity":"2e4aa4c6-9cf9-4dd0-9e3e-53b3b83aa8c7","order_by":28,"name":"Carlos Silvestre-Roig","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Silvestre-Roig","suffix":""},{"id":446689160,"identity":"ee1fbf16-3dad-4dd9-97c6-2ca5a6a752d6","order_by":29,"name":"Malte Kelm","email":"","orcid":"","institution":"Heinrich-Heine-University","correspondingAuthor":false,"prefix":"","firstName":"Malte","middleName":"","lastName":"Kelm","suffix":""},{"id":446689161,"identity":"f84716f9-e02c-4a37-b404-bf5aa59477c1","order_by":30,"name":"Norbert Gerdes","email":"","orcid":"https://orcid.org/0000-0002-4546-7208","institution":"Heinrich-Heine University","correspondingAuthor":false,"prefix":"","firstName":"Norbert","middleName":"","lastName":"Gerdes","suffix":""},{"id":446689162,"identity":"15707058-03b1-49cb-9ab8-62824c7f41ed","order_by":31,"name":"Raphael Chevre","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Raphael","middleName":"","lastName":"Chevre","suffix":""},{"id":446689163,"identity":"89444cf1-f6d3-491c-82c5-622373d0462d","order_by":32,"name":"Stefan Lange","email":"","orcid":"","institution":"University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Lange","suffix":""},{"id":483486954,"identity":"3316f640-20f0-4b4f-8f1c-108e434d3357","order_by":33,"name":"Oliver Soehnlein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIie2PsUoDQRRFX5rYvA+YsC7+wlsWNghBf2UHIdsMmEosI1NsI2w7i37EVqmEDARisx8wss2KEBsLbSSFRGcVNM1E7CzmlI93uPcCeDz/kz7ABBD2pgDp1rk33amQVVB/K5/fvyoA7Cdit3KQy+XDhEb7w8FT0rYw40WQP7YvNxAWDoXqZRYrGuPhlRhSCg0vr+voQq0gLh0xxEQSIC2QGpEwvml4ZURP2mG80o5i6vTVKu9Id3XCupS5ye7lm1XmDgWM6FtFIxn8UiqWRhK6FPeWrtgJUj0+65RYGRGVl5rFylUsl6sAz4+O6XYxG6yhCQuVtc9rPQoLx3wn7I//Ho/H49nmA7GuXIjKpHeCAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7854-0694","institution":"University of Münster","correspondingAuthor":true,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Soehnlein","suffix":""}],"badges":[],"createdAt":"2025-04-17 21:35:10","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6474581/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-6474581/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88046105,"identity":"8e792c5d-77f7-401d-b5bb-94250e7b4c88","added_by":"auto","created_at":"2025-07-31 18:22:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":920296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn-depth neutrophil maturation analysis reveals mobilization of preNeus as a characteristic feature of STEMI associated with survival. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Patient groups and design. (\u003cstrong\u003eB\u003c/strong\u003e) Relative abundance of mature and immature neutrophils. Kruskal-Wallis. (\u003cstrong\u003eC\u003c/strong\u003e) Z-score-normalised surface marker expression in different neutrophil maturation bins. (\u003cstrong\u003eD\u003c/strong\u003e) Proliferation using ex vivo5-Ethynyl-2'-deoxyuridine (EdU) incorporation and detection by flow cytometry. (\u003cstrong\u003eE\u003c/strong\u003e) Giemsa staining of FACS-purified cells of different maturation stage. Scale bar, 10 µm. (\u003cstrong\u003eF\u003c/strong\u003e) Relative abundance and log2-fold change of different neutrophil maturation stages in HC (grey), HF (light-blue), stroke (purple) and STEMI (red) vs HC (grey). Inserts represent magnified immature bins 1-3. Mean ± SEM. (\u003cstrong\u003eG\u003c/strong\u003e) Frequencies of preNeus in blood of different cohorts. Kruskal-Wallis. (\u003cstrong\u003eH\u003c/strong\u003e) UMAP density projection of neutrophils and absolute preNeu counts across different patient subgroups. Mann-Whitney.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6474581/v2/28f9d628521acd7b9f23f3e0.png"},{"id":88046844,"identity":"61faeb38-8df7-450a-b090-0b5f6c5f235b","added_by":"auto","created_at":"2025-07-31 18:30:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10187801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreNeus can be identified as immature granulocytes in clinical blood counts which define a survival-associated subset of patients after STEMI. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Correlation of preNeus identified using flow cytometry with “immature granulocytes” (IGs) from automated blood counter analysis. (\u003cstrong\u003eB\u003c/strong\u003e) FACS-sorting of neutrophil subsets followed by automated blood counter-based identification of IGs and neutrophils. Mean ± SEM. ANOVA. (\u003cstrong\u003eC\u003c/strong\u003e) IGs and neutrophil counts on admission. Mann-Whitney. (\u003cstrong\u003eD\u003c/strong\u003e) Area under the curve (AUC) of receiver-operator characteristic (ROC) for biomarkers of survival.Error: 95% CI. (\u003cstrong\u003eE/J\u003c/strong\u003e) IGs and neutrophil counts on admission. Mann-Whitney. (\u003cstrong\u003eF/K\u003c/strong\u003e) AUC of ROCs for biomarkers of survival. Error: 95% CI. (blue symbol: p \u0026lt; 0.05) (\u003cstrong\u003eG/L\u003c/strong\u003e) ROC curve for IGs. (\u003cstrong\u003eH/M\u003c/strong\u003e) Kaplan Meier curves after division of the cohorts based on IGs. logrank test. Error: 95% CI. (\u003cstrong\u003eI/N\u003c/strong\u003e) logrank Hazard Ratios (HR) of significant biomarkers in F. Error: 95% CI.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6474581/v2/5b32053c823b35463c2da814.png"},{"id":105034835,"identity":"29275aa0-3642-4ef4-b647-27db229547d1","added_by":"auto","created_at":"2026-03-20 07:24:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13693254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6474581/v2/97d2bb6f-187d-43d0-b0a1-e2f331689c9d.pdf"},{"id":88047631,"identity":"a27dfe27-5696-4421-92a8-adf3b63185f7","added_by":"auto","created_at":"2025-07-31 18:46:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1398167,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6474581/v2/05dec3d24715d1d3b2b45121.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Neutrophil progenitor mobilization is a distinct feature of STEMI and predicts survival","fulltext":[{"header":"Main","content":"\u003cp\u003eOver decades, neutrophils were considered bystanders in cardiovascular inflammation. In the last decade, however, this paradigm shifted as numerous studies unveiled causal roles for neutrophils across several inflammatory cardiovascular pathologies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Yet, such analyses typically consider the global neutrophil population and do not discriminate between neutrophil maturation and activation stages. Neutrophils exhibit the highest cellular turnover of all cells in the human body\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. They develop in the bone marrow from specialized stem and progenitor cells, which are collectively referred to as the mitotic pool. Neutrophils develop in a stepwise manner from granulocyte-monocyte progenitors (GMPs). The granulopoietic trajectory continues with proNeu1 which transition to proNeu2 and further to proliferative preNeus, which ultimately give rise to post-mitotic immature and mature neutrophils\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This maturation process is a highly-organized process characterized by the stepwise acquisition of maturation markers (e.g., CD16 and CD10) and loss of retention markers (e.g., CXCR4, CD49d). The circulating neutrophil compartement in steady-state is almost exclusively comprised of mature cells; yet, in situations of inflammation and subsequent emergency granulopoiesis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, immature cells are mobilized to the blood stream. This phenomenon, historically referred to as \"left shift\", reflects the increased release of different stages of neutrophil maturation from the bone marrow in order to meet the elevated peripheral demand.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e We here hypothesized that the appearance of neutrophil maturation stages in the blood stream may in fact resemble a biosensor of disease severity that serves as a predictive biomarker in cardiovascular pathologies.\u003c/p\u003e \u003cp\u003eUsing high dimensional spectral flow cytometry, we profiled the neutrophil maturation status in patients with heart failure (HF), ischemic stroke and ST-elevation myocardial infarction (STEMI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In agreement with previous reports, patients in all three pathologies exhibit signs of emergency granulopoiesis including neutrophilia and the appearance of immature neutrophils (% of CD45\u003csup\u003e+\u003c/sup\u003e cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e). Yet, full spectrum flow cytometry permits deep profiling of neutrophil maturation stages. Based on the expression of CD10 and CD16, we divided neutrophil maturation in 11 bins of continuous neutrophil maturation revealing a progressive and dynamic remodelling of the neutrophil surface composition along maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Interestingly, the most immature cluster exhibited overt expression of the retention molecules CXCR4 and CD49d and incorporated the thymidine analogue 5-Ethynyl-2'-deoxyuridine \u003cem\u003eex vivo\u003c/em\u003e (EdU), thus reflecting the last proliferating neutrophil progenitor, i.e. the preNeu (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In agreement herewith, fluorescence-activated cell sorting (FACS) followed by Giemsa staining revealed a segmented nucleus in CD16\u003csup\u003ehigh\u003c/sup\u003eCD10\u003csup\u003ehigh\u003c/sup\u003e mature neutrophils, a banded nucleus in CD16\u003csup\u003eint\u003c/sup\u003eCD10\u003csup\u003elow\u003c/sup\u003e immature neutrophils and a round nucleus in CD16\u003csup\u003eneg\u003c/sup\u003eCD10\u003csup\u003eneg\u003c/sup\u003e preNeus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Quantification of neutrophil maturation bins in different disease cohorts revealed that preNeus were present in STEMI patients but virtually absent in blood from HF and stroke patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e/G\u003c/b\u003e), suggesting their specific and exacerbated mobilization in this particular disease setting.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHaving identified preNeu mobilization as a specific feature of myocardial ischemia, we systematically assessed the factors potentially involved in this process. To this end, we performed Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) analysis of circulating neutrophils allowing to visualize neutrophil maturation across its entire spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Interestingly, patients admitted for acute chest pain (ACP) (with no signs of myocardial ischemia) showed only low levels of preNeus compared to STEMI patients, indicating that acute pain and associated stress responses may not be causally involved in preNeu mobilization. Analysis of the preNeu counts 3\u0026ndash;6 months after the initial STEMI revealed that preNeu levels returned to normal implying that their mobilization is indeed just a transient state. Subdivision of STEMI patients based on Killip classification showed increased preNeu mobilization in STEMI patients with ischemic heart failure. Finally, analysis of preNeu levels based on 30-day survival after STEMI revealed significantly higher preNeus in non-survivors. In line with this finding, receiver operating curve (ROC) analysis of preNeus as a predictor of survival yielded an area under the curve (AUC) of 0.78 in this exploratory STEMI cohort (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e). Taken together, our findings reveal a strong association of preNeu mobilization with clinical severity and outcome after STEMI.\u003c/p\u003e \u003cp\u003eThe use of complex spectral flow cytometry to identify and quantify circulating preNeus in an emergency setting represents a significant challenge in routine clinical practice. Consequently, correlation analysis of neutrophil maturation with blood parameters gathered in the clinic in the vicinity of the intervention led to the identification of the parameter \"immature granulocytes (IGs)\" available from differential blood cell analysis as a potential surrogate marker (\u003cb\u003eSuppl\u003c/b\u003e. Figure\u0026nbsp;3). Clinical blood counter tests were conducted on the majority of STEMI patients (30/37). Indeed, preNeus identified by spectral flow cytometry analysis correlated with the IGs from the automated blood counter analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To further support the notion that preNeus identified by flow cytometry are indeed IGs in the clinical blood counts, we isolated mature neutrophils, immature neutrophils, and preNeus by FACS based on their CD66b, CD10, and CD16 expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cb\u003eSuppl. Figure\u0026nbsp;4A\u003c/b\u003e). Notably, only sorted preNeus but not mature and immature neutrophils were found in the IG gate of the automated blood counter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cb\u003eSuppl. Figure\u0026nbsp;4B\u003c/b\u003e). Clinical blood counters identify IGs based on scatter characteristics and RNA content. In support of our analyses, RNA content decreases in a step-wise manner during maturation from preNeus to mature neutrophils (\u003cb\u003eSuppl. Figure\u0026nbsp;4C\u003c/b\u003e). Of note, we further sorted eosinophils from these samples to exclude a potential eosinophil contamination by both Giemsa staining and blood counter analysis (\u003cb\u003eSuppl. Figure\u0026nbsp;4D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHaving identified a readily accessible biomarker for approximating circulating preNeus, we proceeded to ascertain its capacity to predict outcome after STEMI. In accordance with the results of our flow cytometric analyses, the levels of IGs were markedly elevated in non-survivors. Indeed, the AUC under the ROC for IGs exceeded that of all other cell types and plasma markers, including a range of established biomarkers such as neutrophil-lymphocyte ratio (NLR), creatine kinase (CK), CK-MB, and lactate dehydrogynase (LDH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e/D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eGiven the limited size of our cohort, we performed retrospective analyses of STEMI patients admitted to the University Hospital M\u0026uuml;nster since 2019 and receiving a differential blood cell count on admission (n\u0026thinsp;=\u0026thinsp;376). Among these, 62 died in the first 30 days after STEMI. Corroborating the findings described above, patients dying within the first 30 days after STEMI exhibited elevated neutrophil and IG counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notably, IGs remained the most effective blood biomarker for prediction of mortality (AUC\u0026thinsp;=\u0026thinsp;0.79, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). After Youden index-based IG cut-off identification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) we analysed the survival of IG\u003csup\u003ehigh\u003c/sup\u003e patients (\u0026ge;\u0026thinsp;0.11 x 10\u003csup\u003e6\u003c/sup\u003e / mL) vs IG\u003csup\u003elow\u003c/sup\u003e patients (\u0026lt;\u0026thinsp;0.11 x 10\u003csup\u003e6\u003c/sup\u003e / mL). Strikingly, IG\u003csup\u003ehigh\u003c/sup\u003e patients had a more than 5-times higher risk of dying (log-rank Hazard Ratio (HR)\u0026thinsp;=\u0026thinsp;5.22), while this risk was less prevalent in patients stratified by total neutrophil counts (HR\u0026thinsp;=\u0026thinsp;3.13), LDH (HR\u0026thinsp;=\u0026thinsp;3.27) or CK-MB (HR\u0026thinsp;=\u0026thinsp;2.67) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH\u003cb\u003e/I\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, we validated these findings in a larger NCT-listed, prospective ongoing cohort of STEMI patients receiving contemporary guideline-directed therapy with complete revascularization\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e including 491 patients of which 50 died within 30 days. In accordance with the findings from the exploratory and derivation cohorts, IGs were elevated in non-survivors and represented the best-performing biomarker to separate survivors from non-survivors (AUC\u0026thinsp;=\u0026thinsp;0.81, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ-L). Furthermore, using the pre-identified cut-off of 0.11 x 10\u003csup\u003e6\u003c/sup\u003e IGs / mL we found that in this cohort IG\u003csup\u003ehigh\u003c/sup\u003e patients had an over 6-times higher risk of death (HR\u0026thinsp;=\u0026thinsp;6.81). IGs in this cohort were also superior to neutrophils (HR\u0026thinsp;=\u0026thinsp;4.25), LDH (HR\u0026thinsp;=\u0026thinsp;3.50), and CK-MB (HR\u0026thinsp;=\u0026thinsp;2.43) at identifying high-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM\u003cb\u003e/N\u003c/b\u003e). In a selected subgroup of the validation cohort, IGs were also analysed on day 1, day 5, and, in survivors, at 6 months after the STEMI. In fact, IG number remained correlated with 30-day mortality even when determined at day 1 or day 5 post AMI (\u003cb\u003eSuppl. Figure\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eInflammation is a key pathogenic driver ensuing upon myocardial ischemia, but immune states and subsequent clinical implications remain poorly defined. Multiomic factor analysis has recently revealed multifaceted rewiring of the immune landscape upon cardiac ischemia\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Yet, the complexity of this analytical approach prohibits its application in an acute clinical setting. Of note, the assessment of neutrophils, whose omics features are not readily accessible by routine transcriptomic workflows, were not assessed in this study. Our dedicated neutrophil-centric analyses indicated that blood from patients with stroke, heart failure and STEMI exhibits non-discriminating features of emergency granulopoiesis including neutrophilia and the appearance of immature neutrophils in the blood which have been linked to adverse outcomes across various conditions including cardiovascular inflammation and infections due to their heightened proinflammatory activity including reactive oxygen species (ROS) production and neutrophil extracellular trap (NET) release\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Our data further indicate the specific mobilization of preNeus in myocardial ischemia. Given their low frequency in blood and their quick disappearance form the circulation, it is unlikely that these cells play a key pathogenic role during the acute insult. Yet, it is intriguing to speculate about potential mechanisms of their release. In our hands, preNeus did not correlate with levels of CK-MB and LDH upon admission suggesting that infarct size may not be the main driver of preNeu mobilization. In addition, patients with chest pain of extracardial origin did not display preNeus in blood suggesting that pain sensation or increased sympathetic activation are not major contributors. Thus, it appears likely that the discharge of preNeus to the blood stream is a measure of an overt, misdirected systemic inflammatory response that clinically manifests as increased mortality.\u003c/p\u003e \u003cp\u003eIn summary, our findings indicate that the presence of preNeus is a characteristic feature of STEMI pathophysiology and identifies a severe patient subgroup associated to decreased 30-day survival. Our data further show that immature granulocytes, readily accessible by automated blood counters in routine clinical laboratories, in fact comprised preNeus and represent a biomarker that is a measure of the inflammatory response after coronary occlusion and outperforms traditional biomarkers in predicting survival after MI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics of flow cytometry patient groups\u003c/h2\u003e \u003cp\u003e Informed consent was obtained from healthy donors and patients with the approval of the Ethics Committee of University of M\u0026uuml;nster (study numbers 2021-424-f-S, 2021-532-f-S, 2023-348-f-S, 2023-489-f-S and 2024-253-f-S). We collected EDTA blood between 2022\u0026ndash;2024 at the University Hospital M\u0026uuml;nster from n\u0026thinsp;=\u0026thinsp;37 STEMI patients before revascularization (age (median (IQR)): 59 (50.75\u0026ndash;69); male (%): 33 (89%)), 29 patients with acute chest pain without troponin dynamics in the emergency department (age (median (IQR)): 54.5 (47.75\u0026ndash;67.25); male (%): 19 (65%)) to assess the potential influence of factors not exclusively associated with MI (e.g., fear of death, stress, pain), 21 ischemic stroke patients within the first 24 h after onset of symptoms (age (median (IQR)): 62 (53.5\u0026ndash;82.5); male (%): 15 (71%)), 67 patients with heart failure with reduced ejection fraction (HFrEF; LVEF\u0026thinsp;\u0026lt;\u0026thinsp;40%; age (median (IQR)): 65.5 (59.5\u0026ndash;75); male (%): 55 (82%)) and 59 healthy controls (age (median (IQR)): 58 (42\u0026ndash;63.5); male (%): 37 (63%)). For all cohorts represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients with anaemia, acute infectious diseases, acute inflammatory diseases or chronic inflammatory diseases; kidney, liver, haematological or coagulation disorders; malignancies; recent trauma; surgery in the last 6 months; severe bleeding requiring a blood transfusion; hormonal treatment or taking anti-inflammatory drugs, genetic disorders or psychiatric abnormalities; pregnancy and lactation were excluded. Blood was processed immediately afterwards to preserve the acute neutrophil phenotype.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetails on derivation STEMI cohort\u003c/h3\u003e\n\u003cp\u003e Anonymised patient information including blood counter analysis, clinical chemistry and survival from STEMI patients at the University Hospital M\u0026uuml;nster between 2019 and 2024 were gathered using a data bank query at the Medical Data Integration Center (MeDIC) of the University M\u0026uuml;nster with the approval of the Ethics Committee of University of M\u0026uuml;nster (study number 2024-185-f-S). Patients were identified as STEMI patients based on their main diagnosis ICD-10 code (I21.0 \u0026ndash; I21.3).\u003c/p\u003e\n\u003ch3\u003eDetails on validation STEMI cohort (SYSTEMI)\u003c/h3\u003e\n\u003cp\u003eAll patients were enrolled in the systemic organ communication in STEMI (SYSTEMI) study (NCT03539133), an open-end prospective cohort study that collects data from STEMI and high-risk non-STEMI patients treated at the University Hospital Duesseldorf.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The study has been approved by the ethics committees of the Heinrich-Heine-University in Duesseldorf, Germany (#5961R), and complies with the Declaration of Helsinki. Informed consent was obtained from all enrolled patients included. Blood used for automated differential blood count was drawn following the AMI diagnosis at hospital admission before starting coronary angiography and revascularization. In a subcohort of patients in that differential blood counts were available throughout the entire time course following AMI, IG dynamics at day 1, day 5 and 6-month follow-up were analysed. Detailed information about the biosampling protocol was previously published.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e STEMI patients admitted to the hospital between 2019 and 2023 of which automated differential blood counts including immature granulocyte count and all other parameters were available were included in the study. Following PCI, all patients received standard medication and were treated according to the European guidelines\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSpectral flow cytometry\u003c/h3\u003e\n\u003cp\u003eFor flow cytometric analysis of leukocytes, 200\u0026micro;l EDTA blood was incubated with red blood cell lysis buffer (Biolegend) for 20 minutes on ice for erythrocyte lysis. After washing, cells were centrifuged and resuspended in HANKs buffer (HBSS w/o Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e, 0.06% BSA, 0.3mM EDTA) and unwanted FcR-involved staining blocked using Human TruStain FcX (Biolegend). Afterwards, cells were incubated with a combination of the following fluorophore-conjugated antibodies for 20 min on ice in Cell Staining buffer (Biolegend) prior to measurement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntigen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFluorophore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVendor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCatalogue#\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlexa Fluor 700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHI30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003c/p\u003e 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colname=\"c1\"\u003e \u003cp\u003eCD114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUV615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMM741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Bioscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e751179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUV661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG46-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Bioscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e612980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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\u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Bioscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e746638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD45RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHI100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM5E2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6C6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Bioscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e744198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD79b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCB3-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Bioscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e743229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBV785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12G5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e306530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD66b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFITC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG10F5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePacific Blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD62L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDREG-56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE- Dazzle594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e302252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE-Vio770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREA954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiltenyi Biotec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130-115-832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD49d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE/Cyanine5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREA954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD11b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerCP-Cy5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICRF44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpark Blue 550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSK7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5E8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPC Cy7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12G5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e306528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUV563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIM3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD Biosciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e748366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE-Dazzle594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHI111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD66b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePacific Blue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG10F5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiolegend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305112\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\u003eData acquisition was performed on a 5L-Cytek\u0026reg; Aurora (Cytek Biosciences) and data were analysed using FlowJo v10.10.0 (BD Bioscience), Rstudio and GraphPad Prism 10 (GraphPad Software, USA). Doublets were excluded using forward and side scatter characteristics and dead cells using DAPI. Neutrophils were identified as CD66b\u003csup\u003e+\u003c/sup\u003eSSC\u003csup\u003ehigh\u003c/sup\u003eCD49d\u003csup\u003elow\u003c/sup\u003e cells, eosinophils as CD66b\u003csup\u003e+\u003c/sup\u003eSSC\u003csup\u003ehigh\u003c/sup\u003eCD49d\u003csup\u003ehigh\u003c/sup\u003e cells, B cells as CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003eCD19\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e\u0026minus;\u003c/sup\u003e cells, T cells as CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003eCD19\u003csup\u003e\u0026minus;\u003c/sup\u003eCD3\u003csup\u003e+\u003c/sup\u003e cells and further divided into CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells while monocytes were defined as CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003eCD19\u003csup\u003e\u0026minus;\u003c/sup\u003eCD3\u003csup\u003e\u0026minus;\u003c/sup\u003eHLA-DR\u003csup\u003ehigh\u003c/sup\u003e CD101\u003csup\u003ehigh\u003c/sup\u003e cells in FlowJo (Suppl. Figure\u0026nbsp;1A). UMAP dimensional reduction was then performed in FlowJo and visualized using R.\u003c/p\u003e\n\u003ch3\u003eCytokine assays\u003c/h3\u003e\n\u003cp\u003eFor the isolation of plasma, EDTA blood was centrifuged at 2,000 \u0026times; g for 15 min at 4\u0026deg;C. Afterward, the supernatant was carefully removed and cryopreserved at \u0026minus;\u0026thinsp;80\u0026deg;C. For analysis of cytokines and inflammatory proteins LEGENDplex assays (740809 and 740590, Biolegend) were performed as described by the manufacturer and data acquisition performed on a CytoFLEX S flow cytometer (4L, Beckman Coulter).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical blood test\u003c/h2\u003e \u003cp\u003e The clinical blood tests were performed according to local standard of care within the first 24h of hospitalization. We involved the following parameters: CK, CK-MB, LDH, neutrophils, lymphocytes, monocytes and immature granulocytes. Differential blood count analyses were performed in the central clinical laboratories using a Sysmex XN9000 (University Hospital D\u0026uuml;sseldorf), XN-9100 or XN-1000 (University Hospital M\u0026uuml;nster) (all SYSMEX EUROPE SE).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell sorting of granulocyte subsets and blood counter analysis\u003c/h3\u003e\n\u003cp\u003eEDTA blood was lysed and FcR-blocked before cells were stained using the following cleavable antibodies: anti-CD66b (FITC, Miltenyi Biotec, 1:50), anti-CD10 (APC, Miltenyi Biotec, 1:50) and anti-CD16 (PerCP, Miltenyi Biotec, 1:50). After mature and immature neutrophils, preNeus and eosinophils were sorted using an FACS Aria III (BD), antibodies were cleaved from the sorted cells and the input using REAlease reagent (Miltenyi Biotec) to not interfere with blood counter analysis. Sorted cells were then measured in a Sysmex XN-1000 (SYSMEX EUROPE SE). Since the system is not designed to identify and annotate isolated cell types, we extracted the .fcs files from the machine and performed the cell type identification using sideward scatter characteristics and side fluorescence signal in FlowJo. For this, we placed the gates for neutrophils, monocytes, lymphocytes, eosinophils and immature granulocytes based on the whole blood and the automatic annotation of these samples in the machine.\u003c/p\u003e\n\u003ch3\u003eGiemsa staining\u003c/h3\u003e\n\u003cp\u003eFlow-sorted cell populations were spun onto object slides using a Cellspin III centrifuge (Thermac, 5 min, 270 x g). Cells were fixed in methanol for 7 min and incubated (45 min) with Giemsa stain (Sigma-Aldrich) before imaging.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProliferation assay\u003c/h2\u003e \u003cp\u003eEDTA blood was lysed and then incubated with Ethynyl-2'-deoxyuridine (EdU, Baseclick, 10 \u0026micro;M) in RPMI medium (Gibco, with 2% FCS) for 1h at 37\u0026deg; C and 5%CO\u003csub\u003e2\u003c/sub\u003e. Cells were then stained using conjugated antibodies before EdU incorporation was detected according to manufacturer instructions (Baseclick) and acquired on a flow cytometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using GraphPad Prism 10 (GraphPad Software) and R. The indicated statistical tests for continuous variables were performed dependent on normal distribution and equality of standard deviation. Statistical tests indicated in the figure legend were used. Data is displayed as boxplots, mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e SEM, or mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 95% confidence interval as indicated in the figure legends.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors used ChatGTP-4o mini (OpenAI) and DeepL Write (DeepL) to improve English language and readability. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to thank Lina Vöcking for technical help, Thorsten König for help with cell sorting and the patients and their families for agreeing to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.S. receives support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (CRC TRR332 projects A2 and Z1, CRC1123 project A6, CRU342 project 1, project 502158695, SO876/16-1), the Leducq Foundation, Novo Nordisk, the EU (PRAETORIAN Doctoral Network), the Else-Kröner Fresenius Stiftung, and the IZKF and the IMF of the University of Münster.\u0026nbsp;C.S.R. is supported by the DFG (CRC TRR332 project A1, CRC1123 project A6) and the IZKF of the University of Münster. J.R receives funding from the DFG (CRU342 project Z, RO4537/5-2). This study was also supported by the DFG - Grant No. 236177352-CRC1116; projects B06, B09 and B12 to M.K., C.J., N.G., and Grant No. 397484323-CRC/TRR259; projects A05 to N.G. This work was supported by the Research Commission of the Medical Faculty of the Heinrich-Heine-University to A.L. (No. 2021-10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.S. advises Novo Nordisk and Roche and receives funding from Novo Nordisk. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSilvestre-Roig, C., Braster, Q., Ortega-Gomez, A. \u0026amp; Soehnlein, O. Neutrophils as regulators of cardiovascular inflammation. \u003cem\u003eNature Reviews Cardiology\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 327-340 (2020). https://doi.org:10.1038/s41569-019-0326-7\u003c/li\u003e\n\u003cli\u003eLuo, J., Thomassen, J. Q., Nordestgaard, B. G., Tybj\u0026aelig;rg-Hansen, A. \u0026amp; Frikke-Schmidt, R. Neutrophil counts and cardiovascular disease. \u003cem\u003eEuropean Heart Journal\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 4953-4964 (2023). https://doi.org:10.1093/eurheartj/ehad649\u003c/li\u003e\n\u003cli\u003eSender, R. \u0026amp; Milo, R. The distribution of cellular turnover in the human body. \u003cem\u003eNature Medicine\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 45-48 (2021). https://doi.org:10.1038/s41591-020-01182-9\u003c/li\u003e\n\u003cli\u003eEvrard, M.\u003cem\u003e et al.\u003c/em\u003e Developmental Analysis of Bone Marrow Neutrophils Reveals Populations Specialized in Expansion, Trafficking, and Effector Functions. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 364-379.e368 (2018). https://doi.org:https://doi.org/10.1016/j.immuni.2018.02.002\u003c/li\u003e\n\u003cli\u003eKwok, I.\u003cem\u003e et al.\u003c/em\u003e Combinatorial Single-Cell Analyses of Granulocyte-Monocyte Progenitor Heterogeneity Reveals an Early Uni-potent Neutrophil Progenitor. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 303-318.e305 (2020). https://doi.org:10.1016/j.immuni.2020.06.005\u003c/li\u003e\n\u003cli\u003eSwann, J. W., Olson, O. C. \u0026amp; Passegu\u0026eacute;, E. Made to order: emergency myelopoiesis and demand-adapted innate immune cell production. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 596-613 (2024). https://doi.org:10.1038/s41577-024-00998-7\u003c/li\u003e\n\u003cli\u003ePalomino-Segura, M., Sicilia, J., Ballesteros, I. \u0026amp; Hidalgo, A. Strategies of neutrophil diversification. \u003cem\u003eNature Immunology\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 575-584 (2023). https://doi.org:10.1038/s41590-023-01452-x\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;nner, F.\u003cem\u003e et al.\u003c/em\u003e SYSTEMI - systemic organ communication in STEMI: design and rationale of a cohort study of patients with ST-segment elevation myocardial infarction. \u003cem\u003eBMC Cardiovascular Disorders\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 232 (2023). https://doi.org:10.1186/s12872-023-03210-1\u003c/li\u003e\n\u003cli\u003ePekayvaz, K.\u003cem\u003e et al.\u003c/em\u003e Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes. \u003cem\u003eNature Medicine\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1696-1710 (2024). https://doi.org:10.1038/s41591-024-02953-4\u003c/li\u003e\n\u003cli\u003eFraccarollo, D.\u003cem\u003e et al.\u003c/em\u003e Expansion of CD10neg neutrophils and CD14+HLA-DRneg/low monocytes driving proinflammatory responses in patients with acute myocardial infarction. \u003cem\u003eeLife\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e66808 (2021). https://doi.org:10.7554/eLife.66808\u003c/li\u003e\n\u003cli\u003eSchulte-Schrepping, J.\u003cem\u003e et al.\u003c/em\u003e Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 1419-1440.e1423 (2020). https://doi.org:10.1016/j.cell.2020.08.001\u003c/li\u003e\n\u003cli\u003eByrne, R. A.\u003cem\u003e et al.\u003c/em\u003e 2023 ESC Guidelines for the management of acute coronary syndromes: Developed by the task force on the management of acute coronary syndromes of the European Society of Cardiology (ESC). \u003cem\u003eEuropean Heart Journal\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 3720-3826 (2023). https://doi.org:10.1093/eurheartj/ehad191\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6474581/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6474581/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInflammatory cardiovascular diseases are the leading cause of death worldwide. Neutrophils, key drivers during inflammation, have consistently been associated with adverse outcomes and mortality in cardiovascular pathologies. Given the uniquely high plasticity and turnover of neutrophils, we profiled the appearance of distinct neutrophil maturation stages as sensitive and specific biomarkers in patients with ST-elevation myocardial infarction (STEMI), heart failure and stroke. Our data reveal a neutrophilia driven by mobilization of immature neutrophils in all groups; however, STEMI patients exhibited selective appearance of CD16\u003csup\u003eneg\u003c/sup\u003eCD10\u003csup\u003eneg\u003c/sup\u003e preNeus, the final mitotic neutrophil progenitor. PreNeus predicted 30-day mortality better than established biomarkers and were identifiable as immature granulocytes in blood counter analysis, hence enabling identification of high-risk STEMI patients upon hospital admission to guide tailored intervention.\u003c/p\u003e","manuscriptTitle":"Neutrophil progenitor mobilization is a distinct feature of STEMI and predicts survival","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-07-31 18:22:06","doi":"10.21203/rs.3.rs-6474581/v2","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cardiovascular-research","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcardiovascres","sideBox":"Learn more about [Nature Cardiovascular Research](https://www.nature.com/natcardiovascres/)","snPcode":"","submissionUrl":"https://mts-natcardiovascres.nature.com/cgi-bin/main.plex","title":"Nature Cardiovascular Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a8998235-a857-4ca7-a839-8c6542b27768","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":47544320,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":47544321,"name":"Health sciences/Diseases/Cardiovascular diseases/Acute coronary syndromes/Myocardial infarction"}],"tags":[],"updatedAt":"2026-05-05T11:31:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 18:22:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6474581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6474581","identity":"rs-6474581","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}