Chronic inflammation is a central - and often underappreciated - driver of atherosclerosis, and by extension of peripheral artery disease, heart failure, and aortic aneurysm. Our research group combines vascular immunobiology, high-dimensional single-cell analytics, and clinical angiology with a shared aim: to improve how these conditions are diagnosed and treated. Blood-derived immune signatures aim to identify high-risk patients at an earlier stage, and machine learning-based methods translate fundamental discoveries directly into clinical practice.
Current Projects
Immunological Adaptation in PAD – NK Cells and Myeloid Modulators
Objective
Why do some patients with peripheral artery disease (PAD) remain asymptomatic despite severe vascular stenosis? Our research group decodes the underlying immunological mechanisms to develop biomarkers and therapeutic approaches that promote natural revascularization. We focus on two key cell populations:
NK cells: A novel GABAergic NK cell population that secretes pro-angiogenic factors in asymptomatic patients and may actively govern collateral circulation.
CD11c+ myeloid cells: Early responders in ischemic tissue. Their frequency in peripheral blood directly correlates with disease severity, establishing them as a highly promising prognostic biomarker.
Clinical Rationale
Individual adaptive capacity determines the risk of amputation and overall quality of life. The immune system is the decisive—yet diagnostically largely ignored—variable in this process.
Clinical Translation
Translation für Patienten
Blood biomarkers for the early detection of high-risk patients
Pharmacological activation of endogenous neovascularization
Long-term goal: NK cell-based immunotherapy for amputation prevention
Methods
Single-cell RNA sequencing (BD Rhapsody), AbSeq (BD), 41-color spectral flow cytometry, single-cell multi-omics, and a murine hindlimb ischemia model. Data acquisition and analysis are performed via the CytoIntelligence Core Facility (CYTO+), directed by Prof. Nording.
Funding
DGA Doctoral Scholarship (T. Lenke) | DZHK Postdoc Startup Grant | DZHK Site Project
Collaborations
Beerens Lab (UKE Hamburg) | Prof. Quirin Hammer (UKSH Kiel) | PD Dr. Anke Fähnrich (University of Lübeck) | Harald F. Langer (UMM Mannheim)
AI-Driven Immune Phenotyping in PAD
Objective
Automated, high-dimensional analysis of immune cells using machine learning to distinguish therapy responders from non-responders in PAD. We develop AI algorithms that utilize flow cytometry data to create scalable predictive models for disease progression and treatment success.
Clinical Rationale
Conventional PAD diagnostics fail to capture the patient’s immune landscape. Subtle immune signatures in peripheral blood—detectable exclusively through AI—can enable personalized, point-of-care therapeutic decisions.
Clinical Translation
Early detection via accessible blood analysis
Targeted guidance for anti-inflammatory therapies
Avoidance of unnecessary interventions in low-risk profiles
Methods & CIC Integration
Machine learning FACS pipeline via the CytoIntelligence Core Facility (CYTO+) - standardizing high-throughput immune phenotyping for clinical scalability.
Funding
PreDigTIM Preparation Grant
CECs & EPCs as Biomarkers in PACNS
Objective
We are developing a blood-based diagnostic tool for Primary Angiitis of the Central Nervous System (PACNS)—a rare but severe inflammatory disease of the cerebral vasculature. Circulating endothelial cells (CECs) and endothelial progenitor cells (EPCs) are quantified and validated using immunomagnetic isolation and 43-color spectral FACS.
Clinical Rationale
PACNS predominantly affects young adults and has traditionally required a brain biopsy for definitive diagnosis. Our blood-based biomarker panel aims to supplement or replace this high-risk invasive procedure.
Clinical Translation
Diagnosis via blood sampling instead of brain biopsy
Earlier initiation of therapy, reducing permanent neurological damage
Reliable differentiation from Reversible Cerebral Vasoconstriction Syndrome (RCVS) and Moyamoya disease
Methods
Immunomagnetic isolation and 43-color spectral FACS via the CytoIntelligence Core Facility (CYTO+), for the Hamburg and Kiel cohorts.
Funding
Seed funding (in application)
Collaboration
Neurovascular Center UKSH (PD Dr. Milani Deb-Chatterji)
The Complement-Platelet Axis as a Therapeutic Target in Chronic Vascular Inflammation
Objective
Investigation of the C5aR1-CXCL4 signaling cascade as a key mechanism for inflammation-driven angiogenesis and arteriogenesis. Platelet C5aR1 stimulates the secretion of anti-angiogenic CXCL4 via C5a and notably exhibits significant sex-specific differences.
Clinical Rationale
Chronic inflammation drives atherosclerosis, heart failure, and PAD. The complement system—long considered purely a defense mechanism—actively regulates neovascularization via platelets. By selectively inhibiting or activating this axis, we aim to “reprogram” inflammation: shifting the balance from tissue damage to tissue repair.
Clinical Translation
Preclinical evaluation of gene-therapy approaches for “no-option” PAD patients (critical limb ischemia without surgical revascularization options)
Pharmacological modulation of the C5a-C5aR1 axis (utilizing available oral antagonists)
Combination with established therapies (statins, antiplatelet agents)
Methods
Hindlimb ischemia model with multi-omics integration (transcriptomics + immune phenotyping + microCT) to drive translation toward first-in-human trials.
Funding
DZHK | Third-Party Fund Schleswig-Holstein
PROGRESS – AI-Assisted Prognosis through Coronary Collateral Analysis
Objective
The EU-funded PROGRESS study employs AI algorithms (Convolutional Neural Networks) to automatically evaluate coronary collateral circulation (CCC) in patients with chronic total occlusion (CTO)—providing an expert-independent prognosis of individual survival benefits.
Clinical Rationale
Well-developed coronary collateral circulation likely improves survival, yet its assessment in routine diagnostics remains unreliable. Our AI algorithm achieves high sensitivity in collateral detection, matching or exceeding cardiovascular expert assessments.
Clinical Translation
Objective, reproducible AI prognosis of survival in severe coronary artery disease
Optimized decision-making framework for revascularization interventions
Reduction of overtreatment and undertreatment in CTO
Funding
Multicenter PROGRESS Consortium (ERA PerMed Initiative 2021-2024)
Collaborations
Tanja Zeller and Zouhair Aherrahrou (University of Lübeck) | Harald F. Langer (UMM Mannheim)
Immune Signatures in Valve Therapies – TAVR & T-TEER
Objective
We analyze how interventional valve therapies—Transcatheter Aortic Valve Replacement (TAVR) and Transcatheter Edge-to-Edge Repair (T-TEER)—alter systemic inflammatory responses and circulating immune cell populations. High-dimensional immune phenotyping before and after the procedure is evaluated using deep learning methodologies.
Clinical Rationale
TAVR and T-TEER trigger complex, largely uncharacterized immune reactions. The trajectory of these responses is prognostically highly relevant and provides a foundation for immunological peri-interventional monitoring.
Clinical Translation
Enhanced pre-interventional risk stratification
AI-based identification of prognostic immune markers
Optimization of treatment timing through immunological monitoring
Methods
High-dimensional immune phenotyping (pre/post intervention) combined with deep-learning analysis utilizing the infrastructure of the CytoIntelligence Core Facility
Collaborations
Derk Frank und Jakob Voran (UKSH Kiel)
Fontan Immunology Biobanking – “Immune Resilience” in Univentricular Hearts
Objective
Patients with a Fontan circulation (univentricular heart, UVH) are subjected to chronic hemodynamic stress: persistently elevated venous pressure and a lack of pulsatility drive maladaptive immune phenotypes (e.g., T-cell exhaustion, autoimmunity), though the severity varies widely among individuals. The SHIELD-HLHS study seeks “immune resilience” signatures capable of breaking this hemodynamic-immune feedback loop to improve long-term survival.
Clinical Rationale
Fontan failure is not solely a mechanical issue—it is a systemic immunological failure. Understanding this immune axis enables targeted, disease-modifying interventions.
Clinical Translation
Identification of protective immune profiles in UVH
Establishment of a foundation for immunomodulatory therapies to extend life expectancy
Long-term goal: Individualized surveillance and management strategies
Methods & CIC Integration
Prospective biobank (n=100) combined with a retrospective DESTATIS analysis (n=10,000). 43-color spectral flow cytometry via the CytoIntelligence Core Facility (CYTO+). Integration with 4D Flow MRI, Computational Fluid Dynamics (CFD), and Multi-Omics Factor Analysis (MOFA).
Status
Active biobank recruitment.
Further Translational Biobanks – KVB & IMPART-AA
Objective
Our research group operates two structured biobank initiatives serving as the translational foundation for biomarker development in vascular diseases.
Kiel Vascular Biology Biobank (KVB)
Systematic collection of biospecimens (urine, stool, serum, plasma, PBMCs) from patients with steno-occlusive vascular diseases. In collaboration with the Department of Rheumatology (Prof. Leipe), we characterize specific immune profiles in vasculitic diseases.
IMPART-AA – IMmune Profiling for Aneurysm Risk Stratification and Treatment Timing
Objective: Clinical validation of immune-based predictors for thoracic, abdominal, and thoracoabdominal aortic aneurysms.
Clinical Rationale: Specific immune cell infiltration patterns correlate with accelerated aneurysm growth; however, standardized blood biomarkers are currently lacking.
Clinical Translation: AI-assisted risk stratification tool for aneurysm progression. Goal: Risk-adapted imaging intervals and optimized surgical timing.
Methods: Transcriptome analysis (mRNA, miRNA, siRNA, circRNA), PBMC phenotyping, and serum/plasma proteomics via the CytoIntelligence Core Facility (CYTO+).
Collaboration
Department of Vascular and Endovascular Surgery
CytoIntelligence Core Facility (CIC / CYTO+)
The CytoIntelligence Core Facility (CIC / CYTO+) is a core research infrastructure directed by Prof. Nording, providing machine learning-assisted fluorescence-activated cell sorting (FACS) for translational cardiovascular research. The facility supports standardised high-throughput immunophenotyping with spectral panels of up to 43 colours and underpins several of the group’s ongoing projects. Analytical capabilities are continuously extended through in-house algorithm development.
