BloodSpot: a database of healthy and malignant haematopoiesis updated with purified and single cell mRNA sequencing profiles
Open accessAbstract
BloodSpot is a gene-centric database of mRNA expression of haematopoietic cells. The web-based interface to the database includes three concomitant levels of visualization for a gene query; foremost is the expression across hematopoietic cell types, second is analysis of survival of Acute Myeloid Leukaemia patients based on gene expression, and lastly, the expression visualized in an interactive developmental tree. With the introduction of single cell data we have now also included an unbiased dimensionality reduction method to show gene expression over the continuum of haematopoiesis. The webserver includes a few select analysis functionalities, like Student's t-test, identification of correlating genes and lookup of whole genetic signatures, with the aim of making generation and testing of hypotheses quick and intuitive. The visualizations have been updated to accommodate new datatypes and the database has been largely expanded with RNA-sequencing datasets, both purified in bulk and at single cell resolution, increasing the number of single samples more than 10 fold, while keeping simplicity in presentation. The database should be of interest for any researcher within leukaemia, haematopoiesis, cellular development, or stem cells. The database is freely available at www.bloodspot.eu.
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limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 1,511 · see citing works
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis
Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., et al. (2011) · Cell · doi:10.1016/j.cell.2011.01.004 · Cited by 942 · see citing works
Human haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · see citing works
From haematopoietic stem cells to complex differentiation landscapes
Laurenti, E., & Göttgens, B. (2018) · Nature · doi:10.1038/nature25022 · Cited by 830 · see citing works
Distinct routes of lineage development reshape the human blood hierarchy across ontogeny
Notta, F., Zandi, S., Takayama, N., Dobson, S., Gan, O. I., Wilson, G., Kaufmann, K. B., McLeod, J., Laurenti, E., Dunant, C. F., et al. (2016) · Science · doi:10.1126/science.aab2116 · Cited by 780 · see citing works
Chromatin state dynamics during blood formation
Lara-Astiaso, D., Weiner, A., Lorenzo-Vivas, E., Zaretsky, I., Jaitin, D. A., David, E., Keren-Shaul, H., Mildner, A., Winter, D., Jung, S., et al. (2014) · Science · doi:10.1126/science.1256271 · Cited by 776 · see citing works
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
Self-citationGenetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works
Self-citationHaferlach, T., Kohlmann, A., Wieczorek, L., Basso, G., Kronnie, G. T., Béné, M. C., Vos, J. D., Hernández‐Rivas, J. M., Hofmann, W., Mills, K., et al. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.23.4732 · Cited by 657 · see citing works
Bagger, F. O., Sasivarevic, D., Sohi, S. H., Laursen, L. G., Pundhir, S., Sønderby, C. K., Winther, O., Rapin, N., & Porse, B. T. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1101 · Cited by 330 · see citing works
Self-citationGene Expression Commons: An Open Platform for Absolute Gene Expression Profiling
Seita, J., Sahoo, D., Rossi, D. J., Bhattacharya, D., Serwold, T. F., Inlay, M. A., Ehrlich, L. I. R., Fathman, J. W., Dill, D. L., & Weissman, I. L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040321 · Cited by 261 · see citing works
Kohlmann, A., Kipps, T. J., Rassenti, L. Z., Downing, J. R., Shurtleff, S. A., Mills, K., Gilkes, A., Hofmann, W., Basso, G., Dell’Orto, M. C., et al. (2008) · British Journal of Haematology · doi:10.1111/j.1365-2141.2008.07261.x · Cited by 220 · see citing works
Ontogeny of erythroid gene expression
Kingsley, P. D., Greenfest‐Allen, E., Frame, J. M., Bushnell, T. P., Malik, J. T., McGrath, K. E., Stoeckert, C. J., & Palis, J. (2012) · Blood · doi:10.1182/blood-2012-04-422394 · Cited by 173 · see citing works
Haemopedia: An Expression Atlas of Murine Hematopoietic Cells
de Graaf, C. A., Choi, J., Baldwin, T. M., Bolden, J. E., Fairfax, K. A., Robinson, A. J., Biben, C., Morgan, C., Ramsay, K. A., Ng, A. P., et al. (2016) · Stem Cell Reports · doi:10.1016/j.stemcr.2016.07.007 · Cited by 89 · see citing works
HemaExplorer: a database of mRNA expression profiles in normal and malignant haematopoiesis
Bagger, F. O., Rapin, N., Theilgaard‐Mönch, K., Kaczkowski, B., Thorén, L., Jendholm, J., Winther, O., & Porse, B. T. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1021 · Cited by 79 · see citing works
Self-citationStemformatics: Visualisation and sharing of stem cell gene expression
Wells, C. A., Mosbergen, R., Korn, O., Choi, J., Seidenman, N., Matigian, N., Vitale, A. M., & Shepherd, J. L. (2012) · Stem Cell Research · doi:10.1016/j.scr.2012.12.003 · Cited by 77 · see citing works
Sidiropoulos, N., Sohi, S. H., Pedersen, T. L., Porse, B. T., Winther, O., Rapin, N., & Bagger, F. O. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1366914 · Cited by 58 · see citing works
Self-citationLeukemia Gene Atlas – A Public Platform for Integrative Exploration of Genome-Wide Molecular Data
Hebestreit, K., Gröttrup, S., Emden, D., Veerkamp, J., Ruckert, C., Klein, H., Müller‐Tidow, C., & Dugas, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039148 · Cited by 55 · see citing works
Differences in Cell Cycle Status Underlie Transcriptional Heterogeneity in the HSC Compartment
Lauridsen, F. K. B., Jensen, T. L., Rapin, N., Aslan, D., Wilhelmson, A. S., Pundhir, S., Rehn, M., Paul, F., Giladi, A., Hasemann, M. S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.057 · Cited by 55 · see citing works
Self-citationBagger, F. O., Rapin, N., Theilgaard‐Mönch, K., et al. (2012) · Blood · doi:10.1182/blood-2012-05-427310 · Cited by 35 · see citing works
Self-citationBloodExpress: a database of gene expression in mouse haematopoiesis
Miranda‐Saavedra, D., De, S., Trotter, M. W. B., Teichmann, S. A., & Göttgens, B. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn854 · Cited by 34 · see citing works
Yu, J., Xing, X., Zeng, L., Sun, J., Li, W., Sun, H., He, Y., Li, J., Zhang, G., Wang, C., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035230 · Cited by 13 · see citing works
Biological Databases for Hematology Research
Zhang, Q., Ding, N., Zhang, L., et al. (2016) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2016.10.004 · Cited by 9 · see citing works
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- [23]Chen L., Ge B., Casale F.P., Vasquez L., Kwan T., Garrido-Martín D., Watt S., Yan Y., Kundu K., Ecker S. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell. 2016; 167: 1398– 1414. 27863251 PMC5119954
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Li, Y., Li, Z., Chen, R., Lian, M., Wang, H., Wei, Y., You, Z., Zhang, J., Li, B., Li, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37213-5 · Cited by 18 · see citing works
- Authors
- Bagger, F. O., Kinalis, S., & Rapin, N.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Bagger, Frederik Otzen
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-05
- Volume
- 47
- Issue
- D1
- Pages
- D881-D885
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6323996
- OpenAlex
- W2900119089
- Cited by
- 229
- References
- 27
- Citations by year
- 2026: 7 · 2025: 22 · 2024: 40 · 2023: 41 · 2022: 39 · 2021: 34 · 2020: 36 · 2019: 10
- FWCI
- 14.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
- Funders
- Lundbeck Foundation (R193-2015-1611, R182-2014-3881)
- Novo Nordisk Foundation (NNF15CC0027852)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- hematopoiesis, gene expression database, single-cell RNA sequencing, acute myeloid leukemia, patient survival, dimensionality reduction, gene expression signatures, leukemia, stem cells
- MeSH terms
- RNA-Seq, Cell Separation, Flow Cytometry, Hematopoiesis, Hematopoietic Stem Cells, Humans, Leukemia, RNA, Messenger, Databases, Genetic, Single-Cell Analysis
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