The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
Cell-type specific gene expression profiles of leukocytes in human peripheral blood
Open accessAbstract
Background: Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays. Results: Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins. Conclusion: The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures






Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Inspired by models and algorithms developed for detecting contamination in DNA sequencing18, demuxlet is fast, accurate, scalable, and compatible with standard input formats17, 19, 20.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [19]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Harrison's Principles of Internal Medicine.
(1988) · Annals of Internal Medicine · doi:10.7326/0003-4819-108-3-499_2 · Cited by 12,528
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Self-citationHarrison's Principles of Internal Medicine
Somberg, J. C. (2005) · American Journal of Therapeutics · doi:10.1097/01.mjt.0000179344.69594.75 · Cited by 2,123
Identifying biological themes within lists of genes with EASE
Hosack, D. A., Dennis, G., Sherman, B. T., Lane, H. C., & Lempicki, R. A. (2003) · Genome biology · doi:10.1186/gb-2003-4-10-r70 · Cited by 1,961
[12] DNA arrays for analysis of gene expression
Eisen, M. B., & Brown, P. O. (1999) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(99)03014-1 · Cited by 1,043
Self-citationIndividuality and variation in gene expression patterns in human blood
Whitney, A. R., Diehn, M., Popper, S. J., Alizadeh, A. A., Boldrick, J. C., Relman, D. A., & Brown, P. O. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252784499 · Cited by 775
Self-citationHigh-fidelity mRNA amplification for gene profiling
Wang, E., Miller, L. D., Ohnmacht, G. A., Liu, E. T., & Marincola, F. M. (2000) · Nature Biotechnology · doi:10.1038/74546 · Cited by 675
Research works printed but not linked: 1
- Kuby J: Immunology. 1997, W. H. Freeman and Company, 3
Cited by
Sorted by citations
Items 201–300 of 328
Hamy, A., Bonsang‐Kitzis, H., Lae, M. E., Moarii, M., Sadacca, B., Pinheiro, A., Galliot, M., Abécassis, J., Laurent, C., & Reyal, F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167397 · Cited by 12
Ríos-Hoyo, A., Xiong, K., Dai, J., Yau, C. S. F., Marczyk, M., García-Milian, R., Wolf, D. M., Huppert, L. A., Nanda, R., Hirst, G. L., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-1553 · Cited by 11
The impact of cesarean delivery on infant DNA methylation
Chen, Q., Ming, Y., Gan, Y., Huang, L., Zhao, Y., Wang, X., Liu, Y., & Zhang, J. (2021) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-021-03748-y · Cited by 11
HLA class II-Restricted CD8+ T cells in HIV-1 Virus Controllers
Nyanhete, T. E., Frisbee, A. L., Bradley, T., Faison, W. J., Robins, E., Payne, T. L., Freel, S. A., Sawant, S. S., Weinhold, K. J., Wiehe, K. J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-46462-8 · Cited by 11
de Nonneville, A. T., Finetti, P., Adélaı̈de, J., Lambaudie, É., Viens, P., Gonçalvès, A., Birnbaum, D., Mamessier, É., & Bertucci, F. (2019) · Cancers · doi:10.3390/cancers11081158 · Cited by 11
Hausburg, M. A., Banton, K. L., Roshon, M. J., & Bar‐Or, D. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05877 · Cited by 11
Building gene expression profile classifiers with a simple and efficient rejection option in R
Benso, A., Carlo, S. D., Politano, G. M. M., Savino, A., & Hafeezurrehman, H. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s13-s3 · Cited by 11
RefBool: a reference-based algorithm for discretizing gene expression data
Jung, S., Hartmann, A., & del Sol, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx111 · Cited by 11
Davies, M. L., Dambaeva, S. V., Katukurundage, D., Repak, M., Gilman‐Sachs, A., Kwak-Kim, J. Y.-H., & Beaman, K. D. (2017) · American Journal of Reproductive Immunology · doi:10.1111/aji.12730 · Cited by 10
The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer
Gonçalvès, A., Finetti, P., Birnbaum, D., & Bertucci, F. (2021) · npj Breast Cancer · doi:10.1038/s41523-021-00256-2 · Cited by 10
Comparative transcriptome analysis of PBMC from HIV patients pre- and post-antiretroviral therapy
Zhao, F., Ma, J., Huang, L., Deng, Y., Li, L., Zhou, Y., Li, J., Li, S., Jiang, H., Yang, H., et al. (2017) · Meta Gene · doi:10.1016/j.mgene.2017.01.004 · Cited by 10
Bradwell, K. R., Coulibaly, D. M., Kone, A. K., Laurens, M. B., Dembélé, A., Tolo, Y., Traoré, K., Niangaly, A., Berry, A. A., Kouriba, B., et al. (2020) · mSystems · doi:10.1128/msystems.00116-20 · Cited by 10
Zebrafish B cell acute lymphoblastic leukemia: new findings in an old model
Park, G., Burroughs-Garcìa, J., Foster, C. A., Hasan, A., Borga, C., & Frazer, J. K. (2020) · Oncotarget · doi:10.18632/oncotarget.27555 · Cited by 11
Kim, S., Lee, H. S., Park, H.-K., Linton, J. A., Lee, J., & Lee, H. (2017) · Chronobiology International · doi:10.1080/07420528.2017.1337780 · Cited by 9
Correlation Between Extended Leukocyte Differential Count and Coronary Artery Disease
Chen, S., Zhang, S., Luan, H., Zeng, X., Li, Y., & Yuan, H. (2018) · Journal of Cardiovascular Pharmacology · doi:10.1097/fjc.0000000000000582 · Cited by 9
Peng, L., Sun, W., Chen, L., & Wen, W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.588454 · Cited by 10
Zhang, J., Jiang, H., Du, K., Xie, T., Wang, B., Chen, C., Cen, B., Yuan, Y., & Ye, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.662460 · Cited by 10
The potential of genetic and gene expression analysis in the diagnosis of neuropsychiatric disorders
van de Leemput, J., Glatt, S. J., & Tsuang, M. T. (2016) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2016.1171714 · Cited by 9
A high-quality annotated transcriptome of swine peripheral blood
Liu, H., Smith, T. P. L., Nonneman, D., Dekkers, J. C. M., & Tuggle, C. K. (2017) · BMC Genomics · doi:10.1186/s12864-017-3863-7 · Cited by 9
Zinc Finger and Interferon-Stimulated Genes Play a Vital Role in TB-IRIS following HAART in AIDS
Ma, J., Zhao, F., Su, W., Li, Q., Li, J., Ji, J., Deng, Y., Zhou, Y., Wang, X., Yang, H., et al. (2018) · Personalized Medicine · doi:10.2217/pme-2017-0084 · Cited by 9
Genetic, Metabolic, and Proteomic Polymorphisms and Clinical Phenotypes of Sepsis
Kovzel, V. A., Davydova, L. A., Lapina, T. A., Semushkina, A. A., & Gutnikov, A. I. (2024) · General Reanimatology · doi:10.15360/1813-9779-2024-6-2470 · Cited by 8
Methods integrating innate and adaptive immune responses in human in vitro immunization assays
Bowley, T. Y., Lenz, K. D., Shanker, A., & Kubicek-Sutherland, J. Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1584852 · Cited by 8
Pan‐cancer analysis of the oncogenic role of HNRNPR in human tumors
Yang, Y., Sun, J., & Xiang, Z. (2023) · Cell Biology International · doi:10.1002/cbin.12027 · Cited by 8
Ye, L., Wang, W., Li, H., Ji, Y., Le, X., & Xu, X. (2024) · Therapeutic Advances in Medical Oncology · doi:10.1177/17588359241290733 · Cited by 8
Immunogenetics of marsupial B-cells
Schraven, A. L., Stannard, H. J., Ong, O. T. W., & Old, J. M. (2019) · Molecular Immunology · doi:10.1016/j.molimm.2019.10.024 · Cited by 8
A blood-based 22-gene expression signature for hepatocellular carcinoma identification
Zheng, J., Zhu, M., Wu, F., Kang, B., Liang, J., Heskia, F., Shan, Y., & Zhang, X. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.01.93 · Cited by 8
Leukocyte p53 protein biosignature through standard-aligned two-dimensional immunoblotting
Hjelle, S. M., Sulen, A., Øye, O. K., Jørgensen, K. M., McCormack, E., Hollund, B. E., & Gjertsen, B. T. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.07.021 · Cited by 8
Using RNA Sequencing to Characterize the Tumor Microenvironment
Smith, C. C., Bixby, L. M., Miller, K. L., Selitsky, S. R., Bortone, D. S., Hoadley, K. A., Vincent, B. G., & Serody, J. S. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9773-2_12 · Cited by 8
Pellegrino-Coppola, D., Claringbould, A., Stutvoet, M. D., Boomsma, D. I., Ikram, M. A., Slagboom, P. E., Westra, H.-J., & Franke, L. (2021) · BMC Genomics · doi:10.1186/s12864-020-07344-w · Cited by 8
Boever, P. D., Wens, B., Boix, J., Felipo, V., & Schoeters, G. E. R. (2013) · Chemical Research in Toxicology · doi:10.1021/tx300510v · Cited by 7
Bommarito, P. A., Martin, E. M., Smeester, L., Palys, T. J., Baker, E., Karagas, M. R., & Fry, R. C. (2017) · Reproductive Toxicology · doi:10.1016/j.reprotox.2017.07.023 · Cited by 7
Raven, N., Klaassen, M., Madsen, T. R. L., Frédéric Thomas, A., Hamede, R., & Újvári, B. (2022) · Molecular Ecology · doi:10.1111/mec.16408 · Cited by 7
Gutiérrez-García, R., del Pozo, T., Suazo, M., Cambiazo, V., & González, M. (2013) · BioMetals · doi:10.1007/s10534-013-9680-9 · Cited by 7
Gene expression shifts in yellow-bellied marmots prior to natal dispersal
Armenta, T. C., Cole, S. W., Geschwind, D. H., Blumstein, D. T., & Wayne, R. K. (2018) · Behavioral Ecology · doi:10.1093/beheco/ary175 · Cited by 7
Sex-specific gene expression in the blood of four primates
Wang, J., Lan, Y., He, L., Tang, R., Li, Y., Huang, Y. C., Shan, L., Gao, Z., Price, M., Yue, B., et al. (2021) · Genomics · doi:10.1016/j.ygeno.2021.06.007 · Cited by 7
Understanding systemic sclerosis through gene expression profiling
Pendergrass, S. A., Whitfield, M. L., & Gardner, H. (2007) · Current Opinion in Rheumatology · doi:10.1097/bor.0b013e3282f00375 · Cited by 7
Peters, E. H., Rojas-Caro, S., Brigell, M., Zahorchak, R. J., Etages, S. A. D., Ruppel, P. L., Knight, C. R., Austermiller, B., Graham, M. C., Wowk, S., et al. (2007) · Clinical Chemistry · doi:10.1373/clinchem.2006.078154 · Cited by 7
O’Connell, G. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-41443-4 · Cited by 6
Vázquez‐Mera, S., Miguéns‐Suárez, P., Martelo‐Vidal, L., Rivas-López, S., Uller, L., Bravo, S. B., Domínguez-Arca, V., Muñoz, X., González‐Barcala, F. J., Nieto‐Fontarigo, J. J., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251910848 · Cited by 6
Gene expression of kynurenine pathway enzymes in depression and following electroconvulsive therapy
Ryan, K. M., Corrigan, M., Murphy, T. M., McLoughlin, D. M., & Harkin, A. (2024) · Acta Neuropsychiatrica · doi:10.1017/neu.2024.34 · Cited by 7
Non-invasive evaluation of the equine gastrointestinal mucosal transcriptome
Coleman, M. C., Whitfield-Cargile, C., Cohen, N. D., Goldsby, J. S., Davidson, L. A., Chamoun‐Emanuelli, A. M., Ivanov, I. V., Eades, S. C., Ing, N. H., & Chapkin, R. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229797 · Cited by 6
Bian, Z., Chen, J., Liu, C. X., Ge, Q., Zhang, M., Meng, J., & Liang, C. Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.052 · Cited by 6
Identifying transcript-level differential expression in primary human immune cells
Mola, S., Beauchamp, C., Boucher, G., Lesage, S., Karaky, M., Goyette, P., Foisy, S., & Rioux, J. D. (2022) · Molecular Immunology · doi:10.1016/j.molimm.2022.12.005 · Cited by 6
Immunomodulatory Functions of the Gastrointestinal Tract
Welcome, M. O. (2018) · Springer eBooks · doi:10.1007/978-3-319-91056-7_10 · Cited by 6
Gene expression profiling of coronary artery disease and its relation with different severities
Kashyap, S., Kumar, S., Agarwal, V., Misra, D. P., Phadke, S. R., & Kapoor, A. (2018) · Journal of Genetics · doi:10.1007/s12041-018-0980-2 · Cited by 6
Immunomagnetic B cell isolation as a tool to study blood cell subsets and enrich B cell transcripts
Henning, A. N., Green, D. S., Baumann, R., Grandinetti, P., Highfill, S. L., Zhou, H., & Giorgi, V. D. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05833-z · Cited by 7
Hohos, N. M., Lee, K. C., Ji, L., Yu, M., Kandasamy, M. K., Phillips, B. G., Baile, C. A., He, C., Schmitz, R. J., & Meagher, R. B. (2016) · Journal of Immunological Methods · doi:10.1016/j.jim.2016.05.003 · Cited by 7
Kalikstad, B., Kultima, H. G., Andersstuen, T. K., Klungland, A., & Isaksson, A. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.6185 · Cited by 5
Saraiva, J., Zubiría-Barrera, C., Klassert, T. E., Lautenbach, M. J., Blaess, M. F., Claus, R. A., Slevogt, H., & König, R. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02366 · Cited by 5
Whitfield, M. L. (2013) · Arthritis & Rheumatology · doi:10.1002/art.38192 · Cited by 5
Considerations for Deconvolution: A Case Study with GTEx Coronary Artery TissuesPreprint
Brehm, Z., Sherina, V., Rosenberg, A. Z., Halushka, M. K., & McCall, M. N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.17.492324 · Cited by 5
Nair, S. S., Chakravarty, D., Balan, S., Hakansson, A. K., Duval, M. X., Davicioni, E., Liu, Y., Bhardwaj, S., Thin, T. H., Garcia-Barros, M., et al. (2025) · Med · doi:10.1016/j.medj.2025.100879 · Cited by 4
Benso, A., Cornale, P., Carlo, S. D., Politano, G. M. M., & Savino, A. (2013) · BioMed Research International · doi:10.1155/2013/676328 · Cited by 4
Felts, S. J., Keulen, V. P. V., Scheid, A. D., Allen, K. S., Bradshaw, R. K., Jen, J., Peikert, T., Middha, S., Zhang, Y., Block, M. S., et al. (2015) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-015-1745-x · Cited by 4
Chan, E. R., Lee, J., Meyerson, H. J., Merkulova, A. A., Smith, S. M., Lin, F., Cho, J. S., & Schmaier, A. H. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022008419 · Cited by 4
The critical role of platelet adenylyl cyclase 6 in hemostasis and thrombosis
Webb, B. A., Cheah, L. T., Khalil, J. S., Hindle, M. S., McKay, M., Turner, N. A., Kearney, M. T., Ariëns, R. A. S., Duval, C., & Naseem, K. M. (2025) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2025.02.045 · Cited by 4
Systems Biology of Infectious Diseases and Vaccines
Nakaya, H. I. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-405926-9.00015-0 · Cited by 4
Weng, D. Y., Chen, J., Taslim, C., Hsu, P.-C., Marian, C., David, S. P., Loffredo, C. A., & Shields, P. G. (2015) · Molecular Carcinogenesis · doi:10.1002/mc.22385 · Cited by 4
Kim, S. S.-Y., Kim, E., Kimm, A. K., & Kwak, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/836841 · Cited by 5
Qiao, Y., Huang, X., Moos, P. J., Ahmann, J. M., Pomicter, A. D., Deininger, M. W., Byrd, J. C., Woyach, J. A., Stephens, D. M., & Marth, G. (2024) · Genome Research · doi:10.1101/gr.278234.123 · Cited by 4
Zhang, H., Shi, G., Hu, Q., Zhang, H., Zheng, M., Jiang, K., & Gu, M. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.08.60 · Cited by 3
Trottier-Lavoie, M., Prunier, J., Poisson, W., Alexandra, C., Isabelle, G., Mastromonaco, G. F., Vicky, A., HERNANDEZ, C., Vincent, B., Taillon, J., et al. (2024) · Forensic Science International Animals and Environments · doi:10.1016/j.fsiae.2024.100093 · Cited by 3
Statistical mechanics approach to the sample deconvolution problem
Riedel, N., & Berg, J. (2013) · Physical Review E · doi:10.1103/physreve.87.042715 · Cited by 3
Hong, G., Chen, B., Li, H., Zhang, W., Zheng, T., Li, S., Shi, T., Ao, L., & Guo, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108104 · Cited by 3
Pharmacogenomics in Rheumatoid Arthritis: How Close are we to the Clinic?
Ponchel, F., Burska, A. N., & Vital, E. M. J. (2014) · Pharmacogenomics · doi:10.2217/pgs.14.79 · Cited by 3
Heterogeneity in Sepsis: New Biological Evidence with Clinical Applications
Leligdowicz, A., & Matthay, M. A. (2019) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-030-06067-1_40 · Cited by 4
Wu, X., Bhatt, V., Liu, J. J., & Hong, L. (2026) · Biochemical and Biophysical Research Communications - BBRC · doi:10.1016/j.bbrc.2026.153273 · Cited by 2
Biomarcadores y endotipos en la sepsis. Nuevas evidencias
Godínez-Vidal, A. R., Carrillo-Esper, R., & Cabello-Aguilera, R. (2021) · Cirugía y Cirujanos · doi:10.24875/ciru.20001734 · Cited by 2
Boruch, A. E., Lindheimer, J. B., Ninneman, J. V., Wylie, G. R., Alexander, T., Klein‐Adams, J. C., Stegner, A. J., Gretzon, N. P., Samy, B., Falvo, M. J., et al. (2023) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2023.100612 · Cited by 2
Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection
Wilk, A. J., Marceau, J. O., Kazer, S. W., Fleming, I., Miao, V. N., Galvez-Reyes, J., Kimata, J. T., Shalek, A. K., Holmes, S., Overbaugh, J. M., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01290-y · Cited by 2
Diverse Epithelial Lymphocytes in Zebrafish Revealed Using a Novel Scale Biopsy Method
Park, G., Foster, C. A., Malone-Perez, M. W., Hasan, A., Macias, J. J., & Frazer, J. K. (2024) · The Journal of Immunology · doi:10.4049/jimmunol.2300818 · Cited by 2
Evaluation of an RNAseq-Based Immunogenomic Liquid Biopsy Approach in Early-Stage Prostate Cancer
Neste, L. V., Wojno, K. J., Henao, R., Mane, S., Korman, H., Hafron, J. M., Kernen, K. M., Tinawi-Aljundi, R., Putzi, M. J., Kassis, A. I., et al. (2021) · Cells · doi:10.3390/cells10102567 · Cited by 2
Aspects of gene expression analysis in small–volume blood samples using the real–time PCR
Hlazunova, O. D., & Melnichuk, N. (2025) · Biopolymers and Cell · doi:10.7124/bc.000b11 · Cited by 1
Manikam, R., Wang, S. M., Yong, Y. K., Raju, C. S., & Sekaran, S. D. (2021) · Journal of Pure and Applied Microbiology · doi:10.22207/jpam.15.4.72 · Cited by 1
PET Imaging of T Cells: Target Identification and Feasibility Assessment
Auberson, Y. P., Briard, E., Rudolph, B., Kaupmann, K., Smith, P. A., & Oberhauser, B. (2018) · ChemMedChem · doi:10.1002/cmdc.201800241 · Cited by 1
Evaluation of biomarkers in the studies of keloid tissue after laser therapy
Mezentsev, A. V., Karapetyan, M. M., Sobolev, V. V., Жукова, О. В., & Korsunskaya, I. M. (2022) · Meditsinskiy sovet = Medical Council · doi:10.21518/2079-701x-2021-21-2-80-85 · Cited by 1
Overview of gene expression techniques with an emphasis on vitamin D related studies
Margret, J. J., & Jain, S. K. (2022) · Current Medical Research and Opinion · doi:10.1080/03007995.2022.2159148 · Cited by 1
Pellegrino-Coppola, D., Claringbould, A., Stutvoet, M. D., Boomsma, D. I., Ikram, M. A., Slagboom, P. E., Westra, H.-J., & Franke, L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.28.120600 · Cited by 1
Gene expression deconvolution and co-expression methods
Cai, C. (2013) · eScholarship (California Digital Library)
Integrative Cardiac Health Project
Vernalis, M., & Nixon, A. H. (2014) · Defense Technical Information Center (DTIC) · doi:10.21236/ada612248
Pereira, T. M. M. (2009) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.60.2009.tde-21082009-092402
Yang, D. S. (2012) · UCL Discovery (University College London)
Targeting PPARs in metabolic risk management: A pharmacological and nutritional approach
Bragt, M. C. E. (2011) · Research Publications (Maastricht University) · doi:10.26481/dis.20111214mb
Lupino, L. (2017) · University of Birmingham Institutional Research Archive (University of Birmingham)
The role of immune regulatory cells in sight-threatening non-infectious uveitis
Zhang, X. (2019) · UCL Discovery (University College London)
Determining the link between facial appearance and immunity in an African population
Phalane, K. G. (2017) · UpSpace Institutional Repository (University of Pretoria)
Novel Approaches for Diagnosing Sepsis in the Critically Ill
Verboom, D. M. (2020) · Utrecht University Repository (Utrecht University) · doi:10.33540/209
Alves, T. L. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.95.2020.tde-17042020-100412
ADAPTIVE IMMUNITY AND THE TUMOR IMMUNE MICROENVIRONMENT
Smith, C. C. (2020) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/0thg-0m30
Increasing the potency of dendritic cell based vaccines for the treatment of cancer
Sika‐Paotonu, D. (2014) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.17142863
Sanz‐Moreno, V. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.732219687.793567355
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa1
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa2
Schneider Arthur, L., Nuno, S.-A., & Barbosa-Morais Nuno, L. (2023) · eLife · doi:10.7554/elife.88623.1.sa0
Biomarkers and sepsis endotypes. New evidence
Godínez-Vidal, A. R., Carrillo-Esper, R., & Cabello-Aguilera, R. (2023) · Cirugía y Cirujanos (English Edition) · doi:10.24875/cirue.m23000358
Additional file 1 of The impact of cesarean delivery on infant DNA methylation
Chen, Q., Ming, Y., Gan, Y., Huang, L., Zhao, Y., Wang, X., Liu, Y., & Zhang, J. (2021) · Figshare · doi:10.6084/m9.figshare.14344168
Metadata
- Authors
- Palmer, C., Diehn, M., Alizadeh, A. A., & Brown, P. O.
- Affiliations
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Brown, Patrick O
- Venue
- BMC Genomics
- Published
- 2006-05-16
- Volume
- 7
- Issue
- 1
- Pages
- 115-115
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC1479811
- OpenAlex
- W2116830437
- Cited by
- 331
- References
- 43
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 13 · 2023: 12 · 2022: 15 · 2021: 27 · 2020: 26 · 2019: 31 · 2018: 23 · 2017: 31 · 2016: 12 · 2015: 16 · 2014: 18 · 2013: 17 · 2012: 15
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Genomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institute of General Medical Sciences (T32GM007365, GM 07365)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- cell type-specific gene expression, leukocytes, B cells, CD4+ T cells, CD8+ T cells, granulocytes, lymphocytes, gene expression signatures, cell surface proteins, ribosomal proteins, inflammatory proteins, peripheral blood
- MeSH terms
- Adult, B-Lymphocytes, Blood Cells, Cell Separation, Female, Genes, Granulocytes, Humans, Leukocytes, Male, Middle Aged, Organ Specificity, CD4-Positive T-Lymphocytes, T-Lymphocyte Subsets, CD8-Positive T-Lymphocytes, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review