I am a Senior Researcher in Bioinformatics at the University of Bergen and leader of the Systems Biology and Bioinformatics node of the Neuro-SysMed Centre for Clinical Treatment Research. My research focuses on the development and application of artificial intelligence and computational methods for precision medicine, with particular emphasis on high-dimensional immune profiling and multimodal biomedical data integration.

My work combines machine learning, statistical modelling, and systems biology approaches to analyse complex biological datasets, including next-generation sequencing data (genomics, transcriptomics, and epigenomics), circulating biomarkers, and high-dimensional single-cell technologies such as mass cytometry (CyTOF) and imaging mass cytometry (IMC). Through these approaches, I aim to identify clinically relevant molecular signatures, understand disease mechanisms, and support the development of data-driven strategies for patient stratification and treatment response prediction.

Throughout my career, I have contributed to the development of novel computational frameworks and open-source software tools applied to problems ranging from gene regulatory element prediction to liquid biopsy analysis and single-cell systems immunology. My current research increasingly focuses on AI-enabled modelling of immune states, spatial tissue organisation, and longitudinal disease trajectories across neurological diseases and cancer.

In parallel with my research activities, I am actively engaged in graduate and interdisciplinary training in computational medicine, contributing to teaching and course development within the CCBIO and Neuro-SysMed research schools. My teaching aims to bridge computational methodology and biomedical application, equipping students and clinicians with the analytical tools required for modern data-driven medicine.

MSToronto 2026 / ECTRIMS-ACTRIMS Joint Meeting
I am pleased to be presenting two studies from our MS research at MSToronto 2026. The first investigates machine-learning-derived single-cell immune-state signatures of inflammatory activity in multiple sclerosis (MSToronto-707), while the second integrates peripheral blood single-cell immune profiling and CSF proteomics to study compartmentalized responses to intrathecal MSC therapy in progressive MS (MSToronto-495).

Together, these studies reflect our ongoing work combining high-dimensional immune profiling, proteomics and computational modelling to better understand inflammatory biology and treatment responses in MS.

Selected outreach activities

• Invited speaker to the Nordic Neuroimmunology meeting organised by Merck, presentation title: "Functional immune-state signatures for understanding inflammatory activity in multiple sclerosis", Oslo, October 2026
• Invited speaker to the 12th national NFCF (Norsk FlowCytometriForening) meeting in collaboration with the University of Tromsø, presentation title: "High-dimensional single cell analyses with applications in precision medicine", June 2024.
• Invited speaker to the annual CCBIO Junior Symbosium, presentation title: "Lets talk about bioinformatics...", June 2023.
• Video campaign about Improved Treatments of Acute Myeloid Leukemia (AML_PM project), Digital Life Norway 2021
• Invited speaker at the Winter Enrichment Program (WEP) 2020, King Abdullah University of Science and Technology, presentation title: "Liquid Biopsies: Progress and Challenges in the New Era of Precision Oncology"
• Research highlight: A closer look at bacteria hijackers, featured by Nature Middle East
• Research highlight: Improved characterization of genetic variation by machine-learning algorithm, publication featured by Nature Middle East

• 2023 to now: Lecturer of the course NeuroSysM930, Applied boinformatics and data analysis in medical research, Department of Clinical Medicine, University of Bergen, Norway
• 2022: Lecturer and organiser of the course BINF200, Analysis of biological sequences and structures, Department of Informatics, University of Bergen, Norway
• 2021: Lecturer and organiser of the course Genomics for Precision Medicine, Topic: Introduction to DNA-Seq processing for cancer data – SNV detection and interpretation, Organised by NORBIS, Norway
• 2020 to now:  Lecturer of cancer research courses, organised by the Centre for Cancer Biomarkers (CCBIO), University of Bergen, Norway. Topics:
  1. CCBIO905: Computational methods for the analysis of mass cytometry imaging data
  2. CCBIO906: Computational methods for copy number variation detection and data interpretation
• 2014-2015 Teaching assistant of MSc/PhD courses of the Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi Arabia

Selected publications

1. Kleftogiannis D et al., Intrathecal mesenchymal stem cell therapy in progressive multiple sclerosis: integrated blood and CSF profiling in the SMART-MS randomized trial, Fluids and Barriers of the CNS (2026)
2. Bjørnstad OV et al., Global and single-cell proteomics view of the co-evolution between neural progenitors and breast cancer cells in a co-culture model, EBioMedicine (2024), doi: 10.1016/j.ebiom.2024.105325
3. Kleftogiannis D et al., Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry, iScience Cell Press, (2024), doi:10.1101/2022.08.13.503587
4. Tornaas S et al., Development of an antibody panel for imaging mass cytometry to investigate cancer-associated fibroblast heterogeneity and spatial distribution in archival tissues, Heliyon Cell Press, (2024), doi: 10.1016/j.heliyon.2024.e31191
5. Ehsani R, Jonassen I, Akslen A and Kleftogiannis D,  LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies, Bioinformatics Advances, (2023), DOI: 10.1093/bioadv/vbad146
6. Kjølle S et al., Hypoxia induced responses are reflected in the stromal proteome of breast cancer, Nature Communications, (2023), DOI: 10.1038/s41467-023-39287-7
7. Tislevoll BS et al., Early response evaluation by single cell signaling profiling in acute myeloid leukemia, Nature Communications, (2023), doi: 10.1038/s41467-022-35624-4
8. Zhu G et al., Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden, Nature Communications, (2021), doi: https://doi.org/10.1038/s41467-021-22463-y
9. Kleftogiannis D et al., Detection of genomic alterations in breast cancer with circulating free DNA sequencing, Scientific Reports, (2020), doi: 10.1038/s41598-020-72818-6.
10. Wu A et al.,  The plasma methylome of metastatic prostate cancer, Journal of Clinical Investigation, (2020), doi: 10.1172/JCI130887.
11. Yogev O et al., In vivo modelling of chemo-resistant neuroblastoma provides new insights into chemo-refractory disease and metastatic progression, Cancer Research, (2019) ,doi:10.1158/0008-5472.CAN-18-2759.
12. Kleftogiannis D et al., Identification of single nucleotide variants using position-specific error estimation in deep sequencing data, BMC Medical Genomics, (2019), doi: 10.1186/s12920-019-0557-9.
13. Kleftogiannis D et al., TELS: a novel computational framework for identifying motif signatures of transcribed enhancers, Genomics, Proteomics & Bioinformatics (2018), doi.org/10.1016/j.gpb.2018.05.003.
14. Mansukhani S et al., Ultra-sensitive mutation detection and genome-wide DNA copy number reconstruction by error corrected circulating tumor DNA sequencing, Clinical Chemistry (2018), doi: 10.1373/clinchem.2018.289629.
15. Kleftogiannis D, Kalnis P, Arner E, Bajic VB, Discriminative identification of promoters and enhancers transcriptional responses after stimulus, Nucleic Acids Research (2017), doi: 10.1093/nar/gkw1015. 
16. Kleftogiannis D, Kalnis P, Bajic VB, Progress and challenges in bioinformatics approaches for enhancer identification, Briefings in Bioinformatics (2016), doi: 10.1093/bib/bbv101. 

I am currently the project leader and  principal investigator of the following projects:

1. Project title: “Comparative Immune Profiling of Multiple Sclerosis Treatments: Towards Stratified Therapy”, Funder: Gerda Meyer Nyquist Gulbrandson & Gerd Meyer Nyquist legat (2025)
2. Project title: “Pilot study on high-dimensional analysis for personalized treatment decisions in multiple sclerosis”, Funder: MS-forbundet (2025)
3. Project title: “Assisting personalised treatment decisions in multiple sclerosis using data-driven immunological signatures”, Funder: Helse Vest (2024)

I am also involved in RAM-MS, a Randomized clinical trial comparing autologous stem cell transplantation versus treatment with alemtuzumab, cladribin or ocrelizumab in patients with relapsing remitting multiple sclerosis. I lead the bioinformatics analysis, and I am responsible for data deposition and management.

In the past I was involved in several precision oncology projects as a WP leader and senior bioinformatician:

• 2019 - 2022: University of Bergen, Project: "Improved Treatments of Acute Myeloid Leukaemias by Personalised Medicine (AML_PM)" funded by ERAPerMed.
• 2021 - 2023: University of Bergen, Project: “Hormone regulators and immune landscape in breast cancer of the young – signature biomarkers for improved diagnosis and outcome”, Funder: Helse Vest.
• 2021 - 2023: University of Bergen, Project: “Nerve involvement in breast cancer”, Funder: Norwegian Cancer Research Society.
• 2019 - Institute of Cancer Research (ICR) – Royal Marsden Cancer Hospital, Project“: CANCEREVO: Deciphering and predicting the evolution of cancer cell populations”, Funder: ERC funded (Consolidator Grant to M Gerlinger)
• 2018 - National Cancer Centre Singapore (SingHealth), Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Project: “CaLiBRe: Cancer Liquid Biopsy for Real-time diagnostics and early intervention”, Funder: A*STAR (National Liquid Biopsy program)