Bioinformatics For Immunology

Characterize cell types, immune repertoires and mechanisms of immune evasion at the highest resolution.

Research into our complex and dynamic defense system benefits from the latest developments in genomic measurement technologies.

What are the key cell types that bring about an immune response? How do they develop? How do they malfunction in autoimmune diseases? And how can the immune system be leveraged to treat cancer? We apply advanced computational analyses to your high-throughput data to help answer questions like these.

Learn more about our expertise and some of the typical computational analyses in immunology, immunobiology and immuno-oncology below. If you would like to benefit from our bioinformatics support, leave us a message!

Leave us a short description of your bioinformatics needs and we will be in touch very soon!

Discover cell types

Studying the composition and functions of the immune system often relies on transcriptomic and epigenomic sequencing.

We analyze RNA-sequencing, single-cell RNA-sequencing and single-cell ATAC sequencing data to

• identify, quantify and compare the types of immune cells in across developmental stages, niches and other conditions,
• characterize the intracellular pathways of immune cells in response to stimulation, and
• identify interactions between cells of the immune system and those between immune cells and other tissues.

Characterize immune repertoires

More targeted immune sequencing approaches can be used to study the repertoire of lymphocyte receptors across conditions or before vs after infection or vaccination. Such data is commonly produced in conjunction with single-cell RNA-seq, enabling associating gene expression to clonal identities.

Uncover host-pathogen interactions

RNA-sequencing of immune cells from pathogen-infected models, in vivo or vitro, allows for characterizing the mechanisms in which the various cell types of the immune system react to antigens, rely information, and neutralize pathogens.

Study tumor immune evasion

In cancer research, (single-cell) RNA-sequencing of tumors enables characterizing their immune microenvironments. Identifying tumor-infiltrating immune cells helps studying the mechanisms and therapeutic opportunities of immune evasion.

Tumor RNA- or DNA-sequencing data can also be used to identify potential neo-antigens for cancer vaccine development.

Learn more

• Discovering targets and biomarkers for immunotherapiesDNA-seq data analysis
• RNA-seq data analysis
• Single-cell RNA-seq data analysis
• Epigenomic data analysis

All analyses

References and case studies

• Investigating vector-borne diseases and pathogen transmission biology with transcriptomics (University of Florida)
• Virtual bioinformatics core service for immunology research (University of Western Australia)
• Discovering cell markers and characterizing microglial response to viral infection (University of Fribourg)
• Detecting tumor immune cells with multiplex immunofluorescence (University of Uppsala)
• Macrophage cell-therapy biomarker discovery (Kuopio Center for Gene and Cell Therapy)
• Single-cell expression analysis (Becton Dickinson)

All references

Selected publications from our customers

• Chang, Y. T. et al. (2024). MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides. Nature communications, 15(1), 752. https://doi.org/10.1038/s41467-024-45083-8
• Adebamowo, S. N. et al. (2024). Genome, HLA and polygenic risk score analyses for prevalent and persistent cervical human papillomavirus (HPV) infections. European journal of human genetics : EJHG, 10.1038/s41431-023-01521-7. Advance online publication. https://doi.org/10.1038/s41431-023-01521-7
• Peeters, J.G.C. et al. (2024). Increased EZH2 function in regulatory T cells promotes their capacity to suppress autoimmunity by driving effector differentiation prior to activation. bioRxiv 2024.04.05.588284; doi: https://doi.org/10.1101/2024.04.05.588284
• Mezheyeuski, A. et al. (2023). An immune score reflecting pro- and anti-tumoural balance of tumour microenvironment has major prognostic impact and predicts immunotherapy response in solid cancers. EBioMedicine, 88, 104452. Advance online publication. https://doi.org/10.1016/j.ebiom.2023.104452
• Tusup, M. et al. (2022). Epitranscriptomics modifier pentostatin indirectly triggers Toll-like receptor 3 and can enhance immune infiltration in tumors. Molecular therapy : the journal of the American Society of Gene Therapy, 30(3), 1163–1170. https://doi.org/10.1016/j.ymthe.2021.09.022
• Cramer, M. et al. (2022). Transcriptomic Regulation of Macrophages by Matrix-Bound Nanovesicle-Associated Interleukin-33. Tissue engineering. Part A, 28(19-20), 867–878. https://doi.org/10.1089/ten.TEA.2022.0006
• Ribeiro, R. et al. (2022). Synchronous Epidermodysplasia Verruciformis and Intraepithelial Lesion of the Vulva is Caused by Coinfection with α-HPV and β-HPV Genotypes and Facilitated by Mutations in Cell-Mediated Immunity Genes. Preprint at https://doi.org/10.21203/rs.3.rs-1991512/v1
• Wullt, B. et al. (2021). Immunomodulation-A Molecular Solution to Treating Patients with Severe Bladder Pain Syndrome?. European urology open science, 31, 49–58. https://doi.org/10.1016/j.euros.2021.07.003
• Åvall-Jääskeläinen, S. et al. (2021). Genomic Analysis of Staphylococcus aureus Isolates Associated With Peracute Non-gangrenous or Gangrenous Mastitis and Comparison With Other Mastitis-Associated Staphylococcus aureus Isolates. Frontiers in microbiology, 12, 688819. https://doi.org/10.3389/fmicb.2021.688819
• Madonna, G. et al. (2021). Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy. Cancers, 13(16), 4164. https://doi.org/10.3390/cancers13164164
• Gurvich, O. L. et al. (2020). Transcriptomics uncovers substantial variability associated with alterations in manufacturing processes of macrophage cell therapy products. Scientific reports, 10(1), 14049. https://doi.org/10.1038/s41598-020-70967-2
• Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599. https://doi.org/10.1002/glia.23741

Selected publications from our team

• Lin, J. et al. (2023). Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection. Nature communications, 14(1), 7630. https://doi.org/10.1038/s41467-023-42763-9
• Caronni, N. et al. (2023). IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer. Nature, 623(7986), 415–422. https://doi.org/10.1038/s41586-023-06685-2
• Papadopoulou, A. et al. (2023). SARS-CoV-2-specific T cell therapy for severe COVID-19: a randomized phase 1/2 trial. Nature medicine, 10.1038/s41591-023-02480-8. Advance online publication. https://doi.org/10.1038/s41591-023-02480-8
• Saralahti, A. K. et al. (2023). Characterization of the innate immune response to Streptococcus pneumoniae infection in zebrafish. PLoS genetics, 19(1), e1010586. Advance online publication. https://doi.org/10.1371/journal.pgen.1010586
• de Sande, A. H. et al. (2023). Cell-type-specific characterization of miRNA gene dynamics in immune cell subpopulations during aging and atherosclerosis disease development at single-cell resolution. bioRxiv 2023.10.09.561173; doi: https://doi.org/10.1101/2023.10.09.561173
• Montaldo, E. et al. (2022). Cellular and transcriptional dynamics of human neutrophils at steady state and upon stress. Nature immunology, 23(10), 1470–1483. https://doi.org/10.1038/s41590-022-01311-1
• Papadopoulou, A. et al. (2022). Robust SARS-COV-2-specific T-cell immune memory persists long-term in immunocompetent individuals post BNT162b2 double shot. Heliyon, 8(7), e09863. https://doi.org/10.1016/j.heliyon.2022.e09863
• Pellegrinelli, V. et al. (2022). Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance. Nature metabolism, 4(4), 476–494. https://doi.org/10.1038/s42255-022-00561-5
• Kron N. S. (2022). In search of the Aplysia immunome: an in silico study. BMC genomics, 23(1), 543. https://doi.org/10.1186/s12864-022-08780-6
• Ballesteros, I. et al. (2020). Co-option of Neutrophil Fates by Tissue Environments. Cell, 183(5), 1282–1297.e18. https://doi.org/10.1016/j.cell.2020.10.003
• Cilenti, F. et al. (2021). A PGE2-MEF2A axis enables context-dependent control of inflammatory gene expression. Immunity, 54(8), 1665–1682.e14. https://doi.org/10.1016/j.immuni.2021.05.016
• Rajamäki, K. et al. (2021). Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease-Associated Colorectal Cancer. Gastroenterology, 161(2), 592–607. https://doi.org/10.1053/j.gastro.2021.04.042
• Cerqueira, J. et al. (2021). Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes. Journal of human genetics, 66(6), 613–623. https://doi.org/10.1038/s10038-020-00888-5
• Loppi, S. et al. (2021). Peripheral inflammation preceeding ischemia impairs neuronal survival through mechanisms involving miR-127 in aged animals. Aging cell, 20(1), e13287. https://doi.org/10.1111/acel.13287
• Jacome Sanz, D. et al. (2021). Proprotein convertase subtilisin/kexin type 9 regulates the production of acute-phase reactants from the liver. Liver international : official journal of the International Association for the Study of the Liver, 41(10), 2511–2522. https://doi.org/10.1111/liv.14993
• Garcia-Manteiga, J. M. et al. (2021). Identification of differential DNA methylation associated with multiple sclerosis: A family-based study. Journal of neuroimmunology, 356, 577600. https://doi.org/10.1016/j.jneuroim.2021.577600
• Lu, Y. et al. (2020). Interleukin-33 Signaling Controls the Development of Iron-Recycling Macrophages. Immunity, 52(5), 782–793.e5. https://doi.org/10.1016/j.immuni.2020.03.006
• Harjula, S. E. et al. (2020). Characterization of immune response against Mycobacterium marinum infection in the main hematopoietic organ of adult zebrafish (Danio rerio). Developmental and comparative immunology, 103, 103523. https://doi.org/10.1016/j.dci.2019.103523
• Lindfors, K. et al. (2020). Metagenomics of the faecal virome indicate a cumulative effect of enterovirus and gluten amount on the risk of coeliac disease autoimmunity in genetically at risk children: the TEDDY study. Gut, 69(8), 1416–1422. https://doi.org/10.1136/gutjnl-2019-319809
• Mehtonen, J. et al. (2020). Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome medicine, 12(1), 99. https://doi.org/10.1186/s13073-020-00799-2
• Harjula, S. E. et al. (2020). Characterization of immune response against Mycobacterium marinum infection in the main hematopoietic organ of adult zebrafish (Danio rerio). Developmental and comparative immunology, 103, 103523. https://doi.org/10.1016/j.dci.2019.103523
• Dufva, O. et al. (2020). Immunogenomic Landscape of Hematological Malignancies. Cancer cell, 38(3), 380–399.e13. https://doi.org/10.1016/j.ccell.2020.06.002
• Lehtipuro, S. et al. (2019). Modes of immunosuppression in glioblastoma microenvironment. Oncotarget, 10(9), 920–921. https://doi.org/10.18632/oncotarget.26643
• Bénéchet, A. P. et al. (2019). Dynamics and genomic landscape of CD8+ T cells undergoing hepatic priming. Nature, 574(7777), 200–205. https://doi.org/10.1038/s41586-019-1620-6
• Luoto, S. et al. (2018). Computational Characterization of Suppressive Immune Microenvironments in Glioblastoma. Cancer research, 78(19), 5574–5585. https://doi.org/10.1158/0008-5472.CAN-17-3714
• Harjula, S. E. et al. (2018). Interleukin 10 mutant zebrafish have an enhanced interferon gamma response and improved survival against a Mycobacterium marinum infection. Scientific reports, 8(1), 10360. https://doi.org/10.1038/s41598-018-28511-w
• Havunen, R. et al. (2018). Abscopal Effect in Non-injected Tumors Achieved with Cytokine-Armed Oncolytic Adenovirus. Molecular therapy oncolytics, 11, 109–121. https://doi.org/10.1016/j.omto.2018.10.005
• Georgolopoulos, G. et al. (2019). Unbiased phenotypic identification of functionally distinct hematopoietic progenitors. Journal of biological research (Thessalonike, Greece), 26, 4. https://doi.org/10.1186/s40709-019-0097-7
• Escobar, G. et al. (2018). Interferon gene therapy reprograms the leukemia microenvironment inducing protective immunity to multiple tumor antigens. Nature communications, 9(1), 2896. https://doi.org/10.1038/s41467-018-05315-0
• Norelli, M. et al. (2018). Monocyte-derived IL-1 and IL-6 are differentially required for cytokine-release syndrome and neurotoxicity due to CAR T cells. Nature medicine, 24(6), 739–748. https://doi.org/10.1038/s41591-018-0036-4
• Heinäniemi, M. et al. (2016). Transcription-coupled genetic instability marks acute lymphoblastic leukemia structural variation hotspots. eLife, 5, e13087. https://doi.org/10.7554/eLife.13087

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