Transcriptome signatures preceding pulmonary arterial thrombosis in critically ill COVID-19 patients

Transcriptome signatures preceding pulmonary arterial thrombosis in critically ill COVID-19 patients | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 September 2025 V1 Latest version Share on Transcriptome signatures preceding pulmonary arterial thrombosis in critically ill COVID-19 patients Authors : Leonoor V. Wismans 0000-0002-7009-7034 , Annemiek van der Eijk , Daniel G. Aynekulu Mersha , Eric C. M. van Gorp , Hayo ter Burg 0009-0005-6606-8424 , Rory D de Vries , Casper H.J. van Eijck , Casper W.F. van Eijck , and Dana A. Mustafa [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175716203.35741931/v1 153 views 92 downloads Contents Abstract Abstract Introduction Discussion Conclusion Tables Figure Legends References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Introduction: Thrombotic complications significantly contribute to morbidity and mortality in patients with severe COVID-19. The pathophysiology of immunothrombosis contributing to pulmonary arterial thrombosis (PAT) in COVID-19 patients is not fully understood. Here, we used targeted gene expression profiling to identify immunological signatures related to the occurrence of PAT in COVID-19 patients admitted to the Intensive Care Unit (ICU). Methods: Blood samples from 27 COVID-19 patients admitted to the ICU were collected from a prospective biobank. Ten samples were collected within seven days before PAT diagnosis (computed tomography angiography [CTA]), and 17 were collected without PAT during admittance (no or negative CTAs). Targeted gene expression profiling was performed using NanoString Technologies. Results: Differences in immune profiles were apparent between patients before PAT diagnosis compared to patients without PAT. The PAT cohort significantly overexpressed genes related to pro-inflammatory pathways, including IL-1 and IL-6 pathways (P < 0.05). The relative abundance of T cells and T cell subtypes (cytotoxic T cells, CD8 T cells, Th1 cells) was lower in the PAT cohort (P. adj < 0.05). Prothrombotic pathways, including oxidative stress, coagulation, and the complement system, were overexpressed in patients who developed PAT (P. adj < 0.05). Conclusions: Our findings suggest that targeted therapies against IL-1 and IL-6 in COVID-19 patients may reduce the risk of PAT. This study also demonstrated that targeted gene expression profiling can identify immune transcriptome signatures in COVID-19 patients preceding PAT. Transcriptome signatures preceding pulmonary arterial thrombosis in critically ill COVID-19 patients Leonoor V. Wismans 1,2,6 , Annemiek A. van der Eijk 3 , Daniel G. Aynekulu Mersha 3,4 , Eric C.M. van Gorp 3,4 , Hayo W. ter Burg 1,6 , Rory D. de Vries 3 , Casper H.J. van Eijck 1,2,6 , Casper W.F. van Eijck 1,2,6 , Dana A.M. Mustafa 1,6 1 Erasmus MC Cancer Institute, Erasmus University Medical Center Rotterdam, University Medical Center Rotterdam, the Netherlands 2 Department of Pulmonology, Erasmus University Medical Center Rotterdam, University Medical Center Rotterdam, the Netherlands 3 Department of Virology, Erasmus University Medical Center Rotterdam, the Netherlands 4 Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands. 5 Department of Intensive Care, Erasmus University Medical Center Rotterdam, the Netherlands 6 Department of Pathology and Clinical Bioinformatics, The Tumor Immuno-Pathology Laboratory, Erasmus University Medical Center Rotterdam, the Netherlands Corresponding author : [email protected] Running title: Transcriptome signatures in COVID-19 thrombosis Keywords : COVID-19, pulmonary embolism, pulmonary arterial thrombosis, immunothrombosis, gene expression profiling Abstract Introduction: Thrombotic complications significantly contribute to morbidity and mortality in patients with severe COVID-19. The pathophysiology of immunothrombosis contributing to pulmonary arterial thrombosis (PAT) in COVID-19 patients is not fully understood. Here, we used targeted gene expression profiling to identify immunological signatures related to the occurrence of PAT in COVID-19 patients admitted to the Intensive Care Unit (ICU). Methods: Blood samples from 27 COVID-19 patients admitted to the ICU were collected from a prospective biobank. Ten samples were collected within seven days before PAT diagnosis (computed tomography angiography [CTA]), and 17 were collected without PAT during admittance (no or negative CTAs). Targeted gene expression profiling was performed using NanoString Technologies. Results : Differences in immune profiles were apparent between patients before PAT diagnosis compared to patients without PAT. The PAT cohort significantly overexpressed genes related to pro-inflammatory pathways, including IL-1 and IL-6 pathways (P < 0.05). The relative abundance of T cells and T cell subtypes (cytotoxic T cells, CD8 T cells, Th1 cells) was lower in the PAT cohort (P. adj < 0.05). Prothrombotic pathways, including oxidative stress, coagulation, and the complement system, were overexpressed in patients who developed PAT (P. adj < 0.05). Conclusions : Our findings suggest that targeted therapies against IL-1 and IL-6 in COVID-19 patients may reduce the risk of PAT. This study also demonstrated that targeted gene expression profiling can identify immune transcriptome signatures in COVID-19 patients preceding PAT. Introduction Thrombotic complications are a major contributor to morbidity and mortality in patients with severe coronavirus disease 2019 (COVID-19), despite the use of routine thromboprophylaxis.(1) The incidence of pulmonary arterial thrombosis (PAT) is as high as 13-27% in COVID-19 patients admitted to the Intensive Care Unit (ICU).(2-4) Compelling histopathologic and imaging evidence indicates that PAT in COVID-19 patients arises as de novo thrombosis in the pulmonary circulation, rather than from emboli in the lower extremities or other circulatory beds.(5, 6) These cases of PAT occur in the absence of deep vein thrombosis and are classified as ‘ in situ PAT’.(5) The pathological entity of in situ PAT is distinct from the ‘classic’ pulmonary embolism (PE), which predominantly results from thromboinflammation.(5-7) The prothrombotic state in patients with severe COVID-19 is attributed to localized bronchoalveolar coagulation and ‘immunothrombosis’.(5, 8) Immunothrombosis in COVID-19 patients is driven by a hyperinflammatory response to severe acute respiratory virus-2 (SARS-CoV-2), resulting in hypercoagulability and activation of platelets and endothelial cells.(8-10) Activated endothelial cells lose their antithrombotic properties, which, combined with complement deposition and platelet aggregation, leads to the preponderance of thrombosis in the pulmonary circulation.(8, 11) Early detection of PAT reduces the risk of serious complications. Despite its importance in clinical practice, prognostic and predictive biomarkers for PAT remain scarce.(9) D-dimer levels are the most sensitive marker for early detection but lack the specificity needed for a definitive diagnosis.(2, 9, 12) The gold standard for diagnosis remains Computed Tomography Angiography (CTA), however in early stages in-situ PAT is not detectable by imaging. These limitations of current practice highlight the need for novel predictive and prognostic biomarkers for PAT in COVID-19 patients. Advanced omics analysis, such as targeted gene expression profiling, may provide a better understanding of the pathophysiology of COVID-19 resulting in PAT and ultimately identify novel biomarkers. ­­ To the best of our knowledge, transcriptome analysis based on the occurrence of PAT in patients with severe COVID-19 has yet to be performed. This study aimed to identify immunological signatures associated with the development of PAT in COVID-19 patients admitted to the ICU. These signatures may identify patients at higher risk of thrombosis and may identify novel targeted therapies . Patient selection Blood samples from 27 COVID-19 patients admitted to the ICU between March and April 2020 (during the first wave of the COVID-19 pandemic) were obtained from a prospective biorepository study (CIUM). The CIUM biobank includes patients with acute respiratory distress syndrome and sepsis in the ICU of Erasmus Medical Centre, The Netherlands. Inclusion criteria were patients with and without proven PAT diagnosis (CTA confirmed), both including PE and in situ PAT diagnosis. The PAT cohort included patients with PAT diagnosis within seven days after sample collection, controls were patients without PAT diagnosis (CTA or negative CTA) during admission. Exclusion criteria included the use of therapeutic anticoagulation due to pre-existing conditions or the inability to undergo CTA due to positioning. The immune transcriptome was assessed in the PAT cohort and controls without PAT ( Supplementary File 1 ). The CIUM study was approved by the Medical Ethical Committee of Erasmus MC Rotterdam (MEC-2017-417) and conducted following the declaration of Helsinki. Written informed consent has been obtained from all patients. Sample collection and preparation Three mL of whole blood was stored in Tempus tubes with 6 mL of stabilizing reagent (Applied Biosystems, Foster City, CA, USA) and at -80°C until RNA extraction. RNA isolation was performed using the Nucleospin RNA Blood kit (the Macherey-Nagel, Bioké, Germany). A total of 400 μL of whole blood was used for RNA extraction, following the manufacturer’s protocol.(13) The quality and quantity of RNA samples was measured by the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). If the RNA concentration was inadequate, further RNA isolation was conducted using an additional 400 μL of whole blood. To correct for degraded RNA, the percentage of fragments of 300–4000 nucleotides was used to calculate the corrected RNA concentrations. NanoString targeted gene expression profiling The Host Response Profiling panel of NanoString Technologies (Seattle, WA, USA) was used for targeted gene expression profiling. The Host Response Profiling panel, developed to understand the complexities of the immune response to infectious diseases, comprises 785 genes across more than 50 immunological pathways. For each sample, 200 ng of RNA was hybridized with probes of the panel at 65°C for 17 hours using SimpliAmp Thermal Cycler (Applied Biosystems). The measurements followed the manufacturer’s protocol using the FLEX nCounter system (NanoString Technologies). Data processing and statistical analysis Data were analyzed using the NanoString nSolver software (version 4.0) and the advanced analysis module (version 2.0). Samples were only included in the analysis if passing the technical quality control steps that were optimized previously.(14) Raw data were normalized using the most stable housekeeping genes included in the panel using the geNorm algorithm in the advanced analysis module (Supplementary File 2). The normalized data were log2 transformed, and the differentially expressed genes were identified using either simplified negative binomial models, a mixture of negative binomial models, or log-linear models based on the convergence of each gene. Immune cell subtypes were identified using marker genes following the pairwise similarity method.(15) Genes were clustered into immunological pathways predefined by NanoString. Pathway alterations were calculated as described in the nCounter Advanced Analysis Manual, resulting in a pathway score for each sample.(16) Immune cell subtype and pathway data were statistically tested between groups using the non-parametric unpaired Mann-Whitney U tests (i.e., Wilcoxon rank sum test). P-values testing was performed using the Benjamin-Hochberg procedure, controlling for the False Discovery Rate (P.adj). Downstream statistical analyses and visualizations for all datasets were performed in R Statistical Software (V.4.10.2) using the ‘EnhancedVolcano’ (v1.11.3), ‘ggplot2’ (v3.4.2), ‘lme4’ (v1.1-32), and ‘rstatix’ (v0.7.2) packages. Results Patient characteristics Samples from 30 patients were included in the study, of which 27 (90%) passed technical quality control. Among these 27 patients, 10 (59%) had a CTA-confirmed PAT within seven days after sample collection (PAT cohort), and 17 (41%) did not undergo CTA or had negative CTAs during admittance (controls) ( Table 1 ). The median age was 68 years (interquartile range (IQR) 61-74) in the PAT cohort compared to 66 years in controls (IQR 56-73). Males represented the majority in both groups, with 90% of the PAT cohort and 76% of the controls (P = 0.62). In the PAT cohort, the median time between sample collection and CTA diagnosis of PAT was 3 days (IQR 2-6). In the PAT cohort, the median time from the onset of COVID-19 symptoms to sample collection was 17 days (IQR 15–21), compared to 16 days (IQR 11–21) in the control group (P = 0.58). A significantly higher proportion of patients in the PAT cohort had hypertension compared to controls (70% vs 18%; P = 0.01). Differentially expressed genes Gene expression analysis revealed 121 differentially expressed immune-related genes between the PAT cohort and controls ( Figure 1A ). None of the differentially expressed genes remained significant after correction for multiple testing (P.adj > 0.05). However, immune cell type profiling showed a significant relative abundance of four immune cell types and pathway analysis revealed 23 significant differentially regulated pathways (P.adj < 0.05) (Figure 1B and 1C). The identified immune signatures and pathways were categorized according to their functional relevance and are discussed in the following paragraphs. Overexpression of pro-inflammatory pathways In the PAT cohort, five pathways related to pro-inflammatory signaling were significantly enhanced: tumor necrosis factor (TNF), Nod-like Receptor (NLR), Toll-Like Receptor (TLR) signaling, IL-1 signaling (P.adj < 0.05) and IL-6 signaling (P.adj = 0.05) ( Figure 2A ). Genes involved in these pathways are illustrated in Figures 2B-2F . Lower T cell Expression and Polarization The relative abundance of T cells ( CD3D+CD3G+CD3E+CD6+TRAT1+SH2D1A ) and subsets: cytotoxic T cells ( KLRD1+ PRF1+ KLRB1+ GZMA+ GNLY+ GZMB+ NKG7+ CTSW+ KLRK1+ GZMH+) and CD8 T cells ( CD8B+ CD8A+ ), and Th1 cells ( TBX21+ ) was significantly lower in the PAT cohort compared to controls (P.adj < 0.05; Figure 3A ). Correspondingly, pathway analysis revealed a considerable decrease in the activity of several T cell-associated pathways ( Figure 3B ), including T cell receptor (TCR) signaling (P.adj = 0.03) and cytotoxicity (P.adj =0.03). Additionally, the Th1 and Th2 differentiation pathways, regulating T cell response polarization, were significantly lower in the PAT cohort compared to controls (P.adj = 0.03). Genes involved in these pathways are illustrated in Figures 3C-3D . These findings suggest a decreased T cell response in patients with PAT compared to those without. The immune exhaustion pathway, including markers for exhausted CD8 T cells ( CD244, EOMES, and LAG3 ), was significantly lower in the PAT cohort (P. adj < 0.05). This suggests that the observed decreased T cell response in patients with PAT was not a result of exhaustion but instead of an impaired or hypofunctional T cell compartment. Overexpression of genes associated with a pro-thrombotic state The PAT cohort significantly overexpressed genes and pathways related to a pro-thrombotic state ( Figure 4A ). Genes in the oxidative stress response pathway were significantly overexpressed (P = 0.04, Figure 4B ). This pathway is linked to endothelial damage and activation of other pro-coagulant pathways. The complement system pathway exhibited significantly enhanced activity in the PAT cohort (P = 0.02; Figure 4D ), corroborated by the overexpression of CR1 (P = 0.004), encoding complement receptor 1. Furthermore, the coagulation pathway was significantly overexpressed in the PAT cohort (P = 0.01, Figure 4D ), with gene F5 (P = 0.04) encoding factor V–a key regulator of the coagulation cascade. Discussion In this study, transcriptomic analysis revealed that pathways associated with pro-inflammatory and pro-thrombotic signaling were significantly overexpressed in patients prior to PAT diagnosis, whereas genes related to T cell responses were markedly underexpressed. In line with previous research, our data on transcriptomic level reveals a hyperactive inflammatory response characterized by releasing interleukins, TNFs, and other mediators.(8, 17, 18) From a clinical perspective, the overexpression of the IL-1 and IL-6 pathways is particularly noteworthy, given the availability of targeted therapies. IL-6 antagonists, such as the monoclonal antibody Tocilizumab, are indicated for treating severe COVID-19. These antagonists have the potential to restore immune dysregulation resembled by the cytokine storm observed in severe COVID-19 and have been shown to reduce mortality risk in hospitalized COVID-19 patients.(19-21) However, the potential impact of IL-6 and IL-1 antagonists on the risk of thrombosis remains a topic of ongoing investigation, as inconsistent outcomes have been reported.(22-24) While Tocilizumab significantly reduced hypercoagulation markers in coagulation and conventional laboratory tests in hospitalized COVID-19 patients, a clear clinical benefit has not been demonstrated.(25, 26) We found a lower abundance of T cells and subtypes, and underexpression of the TCR signaling pathway. Interestingly, exhaustion markers ( CD244, EOMES, LAG3, PDCD1, and CTLA4 ) were not overexpressed in the PAT cohort, suggesting a hypofunctional T cell compartment rather than T cell exhaustion. The observed reduction in circulating T cells may also be explained by their migration into inflamed tissues or secondary lymphoid organs. The resolution of SARS-CoV-2 infection depends largely on the cytotoxic activity of CD8 T cells.(27-29) The functional capacity of the cellular response is therefore a key determinant of clinical outcomes.(30, 31) Lower cytotoxic activity of the T cell response, which was observed in our data, may hinder viral clearance, leading to prolonged inflammation and immune dysregulation, which are known to predispose to thrombotic complications.(8, 27, 28, 30, 31) This suggests the potential role of impaired T cell activity in immunothrombosis. As anticipated, enhancement of pathways associated with oxidative stress, coagulation, and complement was found. The oxidative stress pathway indicates endothelial damage, which has been described as a contributing factor in the pathophysiological cascade in microvascular thrombosis.(5, 8) Activation of these pathways underscores the hypercoagulable state in patients up to seven days before PAT diagnosis. Data from transcriptomic profiling has been used to investigate the pathogenesis of COVID-19.(28, 32-34) Here, we present the first transcriptome analysis based on the occurrence of PAT. Outcomes of this study align with the previously proposed mechanisms of thrombosis, demonstrating the reliably to produce rapid and robust data from peripheral whole blood samples. Therefore, application of targeted gene expression could be more widely adopted in addressing emerging infectious diseases. This study has several limitations. First, the limited sample size of this study reduces the statistical power of this study. Second, our analysis does not distinguish between types of PAT, such in situ PAT and PE, which likely involve distinct mechanisms.(5, 7) Differentiating between in situ PAT and PE based on radiological features is hampered by overlapping characteristics and further complicated by inconsistent terminology in literature.(6) Future clinical and translational studies could focus on cohorts to better understand pathophysiological differences, potentially leading to novel treatment approaches. While both conditions require anticoagulation; patients with in situ PAT may benefit more from additional therapies targeting immune-mediated pathways, such as IL-1 and IL-6, given its association with immunothrombosis. Third, undiagnosed PAT in the control cohort cannot be ruled out. Some cases may have gone undetected due to low clinical suspicion or infeasibility of performing a CTA. Third, this study employs a single-omics approach at the transcriptome level. Incorporating protein validation or integrating additional omics data could have strengthened our findings. . A key strength of this study is that the collected samples capture the end stage of thrombosis, which is a rare window in current clinical practice. Current protocols favor earlier CTAs for PAT screening, allowing for earlier intervention but limiting the opportunity to study the immune system in later disease stages. Furthermore, our analysis provides a unique snapshot of the peripheral immune profile in patients at the beginning of the pandemic, before immunity from prior infection or vaccination could influence immune responses. Conclusion We identified distinct immune transcriptome signatures in COVID-19 patients at the end stage of PAT and underscored the potential of targeted therapies against IL-1 and IL-6. Our study demonstrated that targeted gene expression profiling enables rapid insights into pathophysiological mechanisms, providing a framework for biomarkers, disease monitoring and precision therapies. The integration of this technology will hold significant importance in novel emerging infections, particularly when traditional diagnostics and therapies prove insufficient. Data availability statement Data are available upon reasonable request from the corresponding author. Funding statement This project was funded by the Dutch research council (NOW) for COVID-19 related research projects. Conflict of interest disclosure The authors have declared no conflicts of interest. Ethical approval statement The CIUM study was approved by the Medical Ethical Committee of Erasmus MC Rotterdam (MEC-2017-417) and conducted following the declaration of Helsinki. Patient consent statement Written informed consent has been obtained from all patients. Permission to reproduce material from other sources Not applicable. Clinical trial registration Not applicable. Tables Table 1. Baseline characteristics. n=10 & Patients with no/negative CTA (controls) n=17 P-value Age, years (IQR) 68 (61 - 74) 66 (56 - 73) 0.37 Gender male, total (% of total) 9 (90%) 13 (76%) 0.62 BMI (IQR) 28 (26 - 29) 26 (24 - 32) 0.93 Interval to sample collection, days (IQR) Start COVID-19 related symptoms 17 (15 - 21) 16 (11 - 21) 0.58 Admittance hospital 9 (6 - 11) 6.5 (5 - 8.2) 0.15 Admittance Intensive Care 6.5 (5 - 9) 5 (4 - 6) 0.12 Interval to PE, days (IQR) Sample collection 3 (2 - 6) NA Start COVID-19 related symptoms 21 (16 - 26) NA Admittance hospital 12 (11 - 14) NA Admittance Intensive Care 10 (8 - 12) NA Mechanical ventilation, total (% of total) 10 (100%) 16 (94%) 1.00 Prophylactic dose LMWH, total (% of total) 10 (100%) 17 (100%) 1.00 Comorbidities, total (% of total) Hypertension 7 (70%) 3 (18%) 0.01 Diabetes Mellitus 4 (40%) 3 (18%) 0.37 Pulmonary disease 2 (20%) 4 (24%) 1.00 Cardiovascular disease 1 (10%) 2 (12%) 1.00 Neurological disease 0 2 (12%) 0.52 Malignancy 0 2 (12%) 0.52 Figure Legends Figure 1. Differentially expressed genes, immune cell types and pathways between PAT cohort and controls. Volcano plot displaying each gene’s–log10(P-value) and log2fold change. 121 genes were differentially expressed; none persisted after Benjamin-Hochberg correction for multiple tests. Line indicates P-value < 0.05 Figure 2. Overexpression of pro-inflammatory pathways. A: Pathways associated with pro inflammatory signaling, which were overexpressed in the PAT cohort compared to controls. B: Selected genes in interleukin 1 pathway. C: Selected genes in interleukin 6 pathway. D: Selected genes in toll-like receptor signaling pathway. E: Selected genes in tumor necrosis factor signaling pathway. F: selected genes in NOD like receptor signaling pathway. * indicates P-value <0.05 ** <0.001 Figure 3. T cell expression and polarization. A: Relative abundance of significant differently expressed immune cell types and subtypes. B: Pathways related to T cell response, which were lower expressed in PAT cohort compared to controls. C: Selected genes in cytotoxicity pathway. D: Selected genes in Th1/Th2 differentiation pathways. E: Selected genes in TCR signaling pathway. * indicates P-value <0.05 ** <0.001 Figure 5 . Pathways associated with a pro-thrombotic state . A: Pathways related to pro-thrombotic state, which were overexpressed in the PAT cohort compared to controls. B: Selected genes in oxidative stress response pathway. C: Selected genes in complement system pathway. D: Selected genes in coagulation pathway. * indicates P-value <0.05 ** <0.001 References 1. 1. Investigators R-C, Investigators AC-a, Investigators A, Goligher EC, Bradbury CA, McVerry BJ, et al. Therapeutic Anticoagulation with Heparin in Critically Ill Patients with Covid-19. N Engl J Med. 2021;385(9):777-89.2. Suh YJ, Hong H, Ohana M, Bompard F, Revel MP, Valle C, et al. 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Blood transcriptome responses in patients correlate with severity of COVID-19 disease. Front Immunol. 2022;13:1043219. Google Scholar Information & Authors Information Version history V1 Version 1 06 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Leonoor V. Wismans 0000-0002-7009-7034 Erasmus MC View all articles by this author Annemiek van der Eijk Erasmus MC View all articles by this author Daniel G. Aynekulu Mersha Erasmus MC View all articles by this author Eric C. M. van Gorp Erasmus MC View all articles by this author Hayo ter Burg 0009-0005-6606-8424 Erasmus MC View all articles by this author Rory D de Vries Erasmus MC View all articles by this author Casper H.J. van Eijck Erasmus MC View all articles by this author Casper W.F. van Eijck Erasmus MC View all articles by this author Dana A. Mustafa [email protected] Erasmus MC View all articles by this author Metrics & Citations Metrics Article Usage 153 views 92 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Leonoor V. Wismans, Annemiek van der Eijk, Daniel G. Aynekulu Mersha, et al. Transcriptome signatures preceding pulmonary arterial thrombosis in critically ill COVID-19 patients. Authorea . 06 September 2025. DOI: https://doi.org/10.22541/au.175716203.35741931/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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