Skip Navigation


NAR Molecular Biology Database Collection entry number 1646
Frederik Otzen Bagger

Database Description

BloodSpot is a database that provides gene expression profiles of genes and gene signatures in healthy and malignant hematopoiesis and includes data from both humans and mice. In addition to the default plot, that displays an integrated expression plot, two additional levels of visualization are available; an interactive tree showing the hierarchical relationship between the samples, and a Kaplan-Meier survival plot. The database is sub-divided into several datasets that are accessible for browsing.


This work was funded by the Danish Research Council for Strategic Research and by the NovoNordisk Foundation.


Rapin, N. et al. Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients. Blood 123, 894-904 ( 2014).
2. Majeti, R. et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proceedings of the National Academy of Sciences 106, 3396-3401 (2009).
3. Andersson, A., Edén, P., Olofsson, T. and Fioretos, T. Gene expression signatures in childhood acute leukemias are largely unique and distinct from those of normal tissues and other malignancies. BMC Medical Genomics 3, 6 (2010).
4. Hu, X. et al. Integrated Regulation of Toll-like Receptor Responses by Notch and Interferon-gamma Pathways. Immunity 29, 691-703 (2008).
5. Wildenberg, M. E., van Helden-Meeuwsen, C. G., van de Merwe, J. P., Drexhage, H. A. and Versnel, M. A. Systemic increase in type I interferon activity in Sjögren's syndrome: A putative role for plasmacytoid dendritic cells. European Journal of Immunology 38, 2024-2033 (2008).
7. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296-309 (2011).
8. Di Tullio, A. et al. CCAAT/enhancer binding protein alpha (C/EBP(alpha))-induced transdifferentiation of pre-B cells into macrophages involves no overt retrodifferentiation. Proceedings of the National Academy of Sciences 108, 17016-17021 (2011).
9. Chambers, S. M. et al. Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell 1, 578-591 (2007).
10. Painter, M. W. et al. Transcriptomes of the B and T lineages compared by multiplatform microarray profiling. Journal of immunology (Baltimore, Md. : 1950) 186, 3047-3057 (2011).
11. Desch, A. N. et al. CD103+ pulmonary dendritic cells preferentially acquire and present apoptotic cell-associated antigen. The Journal of experimental medicine 208, 1789-1797 (2011).
12. Malhotra, D. et al. Transcriptional profiling of stroma from inflamed and resting lymph nodes defines immunological hallmarks. Nature immunology 13, 499-510 (2012).
13. Narayan, K. et al. Intrathymic programming of effector fates in three molecularly distinct [gamma][delta] T cell subtypes. Nature immunology 13, 511-518 (2012).
14. Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nature immunology 13, 888-899 (2012).
15. Kohlmann, A. et al. Gene expression profiling in AML with normal karyotype can predict mutations for molecular markers and allows novel insights into perturbed biological pathways. Leukemia 24, 1216-1220 (2010).
16. Kohlmann, A. et al. An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase. British Journal of Haematology 142, 802-807 (2008).
17. Haferlach, T. et al. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. Journal of Clinical Oncology 28, 2529-2537 (2010).
18. Verhaak, R. G. W. et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica 94, 131-134 (2009).
19. de Jonge, H. J. M. et al. High VEGFC expression is associated with unique gene expression profiles and predicts adverse prognosis in pediatric and adult acute myeloid leukemia. Blood 116, 1747-1754 (2010).
20. Network, C. G. A. R. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. 368, 2059-2074 (2013).

Oxford University Press is not responsible for the content of external internet sites