Brain Cell Type Specific Gene Expression and Co-expression Network Architectures
Open accessAbstract
Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.
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
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?
Cell clusters were annotated manually using marker genes conserved between both conditions as astrocytes, microglia, neurons, endothelial cells, ependymal cells, and mixed glia.
“The cell clusters were annotated manually using marker genes that were conserved between both conditions as astrocytes, microglia, neurons, endothelial cells, ependymal cells, and mixed glia (Supplementary Data 1) (Arneson et al., 2018;McKenzie et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as McKenzie et al., 2018
The Discussion (page unavailable) says the paper annotated marker genes for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and OPCs. It therefore supports using its markers for four of the six cell labels in the citing sentence, but does not supply markers for ependymal cells or mixed glia. The manual annotation and conservation between conditions describe the citing authors’ procedure, not a finding established by this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCanonical CNS cell type markers were compiled from previous studies66-69.
“Canonical CNS cell type markers were compiled from previous studies66–69.”
From APOE4 to APOE2 allelic switching in mice improves Alzheimer’s disease-related metabolic signatures, neuropathology and cognition (2025) · cites this paper as [66]
Cell-type proportions of bulk tissue RNA-seq data were estimated by performing cell-type deconvolution on each sample using brain cell-type marker signatures provided by the BRETIGEA R package.
“To estimate the cell-type proportion of bulk tissue RNA-seq data used in this study, we perform a cell-type deconvolution on each sample using the brain cell–type marker signatures provided by the BRETIGEA R package (26).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [26]
All cell clusters were annotated manually based on marker genes that were conserved between the saccharin group and the CNO group and gene lists present in literature.
“All cell clusters were annotated manually based on marker genes that were conserved between the saccharin group and the CNO group and gene lists present in literature (Arneson et al., 2018;McKenzie et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as McKenzie et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219 · see citing works
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Astrocytes: Biology and pathology
Sofroniew, M. V., & Vinters, H. V. (2010) · Acta Neuropathologica · doi:10.1007/s00401-009-0619-8 · Cited by 5,196 · see citing works
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · see citing works
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Role of platelet-derived growth factors in physiology and medicinePreprint
Andræ, J., Gallini, R., & Betsholtz, C. (2008) · Genes & Development · doi:10.1101/gad.1653708 · Cited by 2,443 · see citing works
Zhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · see citing works
Self-citationA survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401 · see citing works
Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system
Marques, S., Zeisel, A., Codeluppi, S., van Bruggen, D., Mendanha Falcão, A., Xiao, L., Li, H., Häring, M., Hochgerner, H., Romanov, R. A., et al. (2016) · Science · doi:10.1126/science.aaf6463 · Cited by 1,246 · see citing works
Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956 · see citing works
Cell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 871 · see citing works
Derivation of blood-brain barrier endothelial cells from human pluripotent stem cells
Lippmann, E. S., Azarin, S. M., Kay, J. E., Nessler, R. A., Wilson, H. K., Al-Ahmad, A., Palecek, S. P., & Shusta, E. V. (2012) · Nature Biotechnology · doi:10.1038/nbt.2247 · Cited by 800 · see citing works
Neuron-glia metabolic coupling and plasticity
Magistretti, P. J. (2006) · Experimental Physiology · doi:10.1113/expphysiol.2010.053157 · Cited by 739 · see citing works
NeuroMorpho.Org: A Central Resource for Neuronal Morphologies
Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2055-07.2007 · Cited by 664 · see citing works
Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
Chen, R., Wu, X., Jiang, L., & Zhang, Y. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.03.004 · Cited by 592 · see citing works
Oligodendroglial NMDA receptors regulate glucose import and axonal energy metabolism
Saab, A. S., Tzvetavona, I. D., Trevisiol, A., Baltan, S., Dibaj, P., Kusch, K., Möbius, W., Goetze, B., Jahn, H. M., Huang, W., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.05.016 · Cited by 590 · see citing works
Shen, S., Sandoval, J., Swiss, V. A., Li, J., Dupree, J. L., Franklin, R. J. M., & Casaccia‐Bonnefil, P. (2008) · Nature Neuroscience · doi:10.1038/nn.2172 · Cited by 486 · see citing works
Nagelhus, E. A., Horio, Y., Inanobe, A., Fujita, A., Haug, F. Š., Nielsen, S., Kurachi, Y., & Ottersen, O. P. (1999) · Glia · doi:10.1002/(sici)1098-1136(199903)26:1 · Cited by 472 · see citing works
Immune surveillance in the central nervous system
Ousman, S. S., & Kubes, P. (2012) · Nature Neuroscience · doi:10.1038/nn.3161 · Cited by 433 · see citing works
Efficient Test and Visualization of Multi-Set Intersections
Wang, M., Zhao, Y., & Zhang, B. (2015) · Scientific Reports · doi:10.1038/srep16923 · Cited by 395 · see citing works
Self-citationEpigenetic regulation of gene expression in physiological and pathological brain processes
Gräff, J., Kim, D., Dobbin, M. M., & Tsai, L.-H. (2011) · Physiological Reviews · doi:10.1152/physrev.00012.2010 · Cited by 373 · see citing works
Multiscale Embedded Gene Co-expression Network Analysis
Song, W., & Zhang, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004574 · Cited by 357 · see citing works
Self-citationMechanisms of glutamate release from astrocytes
Malarkey, E. B., & Parpura, V. (2007) · Neurochemistry International · doi:10.1016/j.neuint.2007.06.005 · Cited by 356 · see citing works
Haj‐Yasein, N. N., Vindedal, G. F., Eilert‐Olsen, M., Gundersen, G. A., Skare, Ø., Laake, P., Klungland, A., Thorén, A. E., Burkhardt, J. M., Ottersen, O. P., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110655108 · Cited by 344 · see citing works
Untangling the brain’s neuroinflammatory and neurodegenerative transcriptional responses
Srinivasan, K., Friedman, B. A., Larson, J. L., Lauffer, B. E., Goldstein, L. D., Appling, L. L., Borneo, J., Poon, C., Ho, T., Cai, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11295 · Cited by 336 · see citing works
Computational deconvolution: extracting cell type-specific information from heterogeneous samples
Shen-Orr, S. S., & Gaujoux, R. (2013) · Current Opinion in Immunology · doi:10.1016/j.coi.2013.09.015 · Cited by 280 · see citing works
Glial Cells as Progenitors and Stem Cells: New Roles in the Healthy and Diseased Brain
Dimou, L., & Götz, M. (2014) · Physiological Reviews · doi:10.1152/physrev.00036.2013 · Cited by 263 · see citing works
Proteolipid Protein Is Required for Transport of Sirtuin 2 into CNS Myelin
Werner, H. B., Kuhlmann, K., Shen, S., Uecker, M., Schardt, A., Dimova, K. P., Orfaniotou, F., Dhaunchak, A. S., Brinkmann, B. G., Möbius, W., et al. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1254-07.2007 · Cited by 259 · see citing works
Individuality and Variation of Personal Regulomes in Primary Human T Cells
Qu, K., Zaba, L. C., Giresi, P. G., Li, R., Longmire, M. R., Kim, Y. H., Greenleaf, W. J., & Chang, H. Y. (2015) · Cell Systems · doi:10.1016/j.cels.2015.06.003 · Cited by 151 · see citing works
Neurulation and neurite extension require the zinc transporter ZIP12 ( <i>slc39a12</i> )
Chowanadisai, W., Graham, D. M., Keen, C. L., Rucker, R. B., & Messerli, M. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222142110 · Cited by 140 · see citing works
Neuropathological and transcriptomic characteristics of the aged brain
Miller, J. A., Guillozet-Bongaarts, A., Gibbons, L. E., Postupna, N., Renz, A., Beller, A. E., Sunkin, S. M., Ng, L., Rose, S. E., Smith, K. A., et al. (2017) · eLife · doi:10.7554/elife.31126 · Cited by 134 · see citing works
Iafrati, J., Orejarena, M. J., Lassalle, O., Bouamrane, L., & Chavis, P. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.66 · Cited by 132 · see citing works
Gokhan, S., Marin-Husstege, M., Yung, S.-Y., Fontanez, D. E., Casaccia-Bonnefil, P., & Mehler, M. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.1850-05.2005 · Cited by 123 · see citing works
Chikina, M. D., Zaslavsky, E., & Sealfon, S. C. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv015 · Cited by 118 · see citing works
Functional Characterization of DNA Methylation in the Oligodendrocyte Lineage
Moyon, S., Huynh, J. L., Dutta, D. J., Zhang, F., Ma, D., Yoo, S., Lawrence, R., Wegner, M., John, G. R., Emery, B., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.03.060 · Cited by 105 · see citing works
Self-citationCell Type-Specific Transcriptomics in the Brain
Okaty, B. W., Sugino, K., & Nelson, S. B. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0626-11.2011 · Cited by 100 · see citing works
Shapiro, L. A., Perez, Z. D., Foresti, M. L., Arisi, G. M., & Ribak, C. E. (2009) · Brain Research · doi:10.1016/j.brainres.2009.02.031 · Cited by 90 · see citing works
Swanson, L. W., & Bota, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1015128107 · Cited by 88 · see citing works
Kohno, H., Maeda, T., Perusek, L., Pearlman, E. M., & Maeda, A. (2014) · The Journal of Immunology · doi:10.4049/jimmunol.1301738 · Cited by 80 · see citing works
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76 · see citing works
NeuroLex.org: an online framework for neuroscience knowledge
Larson, S., & Martone, M. E. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00018 · Cited by 71 · see citing works
Verge: A Novel Vascular Early Response Gene
Regard, J. B., Scheek, S., Borbiev, T., Lanahan, A. A., Schneider, A., Demetriades, A.-M., Hiemisch, H., Barnes, C. A., Verin, A. D., & Worley, P. F. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.4252-03.2004 · Cited by 69 · see citing works
Duteil, D., Chambon, C., Ali, F., Malivindi, R., Zoll, J., Kato, S., Gény, B., Chambon, P., & METZGER, D. (2010) · Cell Metabolism · doi:10.1016/j.cmet.2010.09.016 · Cited by 69 · see citing works
Hsu, M.-P., Frausto, R. F., Rose‐John, S., & Campbell, I. L. (2014) · Glia · doi:10.1002/glia.22739 · Cited by 67 · see citing works
Kitai, R., Zhao, M., Zhang, N., Hua, L. L., & Lee, S. C. (2000) · Journal of Neuroimmunology · doi:10.1016/s0165-5728(00)00315-5 · Cited by 67 · see citing works
Scarr, E., Udawela, M., Greenough, M. A., Neo, J. H., Seo, M. S., Money, T., Upadhyay, A., Bush, A. I., Everall, I., Thomas, E. A., et al. (2016) · Schizophrenia · doi:10.1038/npjschz.2016.2 · Cited by 61 · see citing works
Wang, J., Xia, S., Arand, B., Zhu, H., Machiraju, R., Huang, K., Ji, H., & Qian, J. X. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004892 · Cited by 50 · see citing works
Cell population-specific expression analysis of human cerebellum
Kuhn, A., Kumar, A., Beilina, A. G., Dillman, A., Cookson, M., & Singleton, A. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-610 · Cited by 46 · see citing works
Majumder, A., Banerjee, S., Harrill, J., Machacek, D. W., Mohamad, O., Bacanamwo, M., Mundy, W. R., Wei, L., Dhara, S. K., & Stice, S. L. (2012) · Stem Cells · doi:10.1002/stem.1201 · Cited by 43 · see citing works
Electrical signalling properties of oligodendrocyte precursor cells
Bakiri, Y., Attwell, D., & Káradóttir, R. T. (2009) · Neuron Glia Biology · doi:10.1017/s1740925x09990202 · Cited by 37 · see citing works
Designing and Troubleshooting Immunopanning Protocols for Purifying Neural CellsPreprint
Barres, B. A. (2014) · Cold Spring Harbor Protocols · doi:10.1101/pdb.ip073999 · Cited by 28 · see citing works
Examination of the zinc transporter gene, SLC39A12
Bly, M. (2005) · Schizophrenia Research · doi:10.1016/j.schres.2005.07.039 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works
Self-citationCholesterol and matrisome pathways dysregulated in astrocytes and microglia
TCW, J., Qian, L., Pipalia, N. H., Chao, M. J., Liang, S. A., Shi, Y., Jain, B. R., Bertelsen, S. E., Kapoor, M., Marcora, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.017 · Cited by 411 · see citing works
Self-citationZhao, J., Fu, Y., Yamazaki, Y., Ren, Y., Davis, M. D., Liu, C.-C., Lu, W., Wang, X., Chen, K., Cherukuri, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19264-0 · Cited by 374 · see citing works
Transcriptional signature in microglia associated with Aβ plaque phagocytosis
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works
Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders
Seidlitz, J., Nadig, A., Liu, S., Bethlehem, R. A. I., Vértes, P. E., Morgan, S. E., Váša, F., Romero-García, R., Lalonde, F. M., CLASEN, L. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17051-5 · Cited by 322 · see citing works
Neff, R. A., Wang, M., Vatansever, S., Guo, L., Ming, C., Wang, Q., Wang, E., Horgusluoglu-Moloch, E., Song, W.-M., Li, A., et al. (2021) · Science Advances · doi:10.1126/sciadv.abb5398 · Cited by 306 · see citing works
“Previous work by our laboratory has shown that brain cell–type surrogate proportion values (SPVs), including the proportion of neurons in a sample, can be inferred from bulk RNA-seq data when combined with measurements of brain cell type–specific gene expression patterns (26) and may serve as a marker of neuronal loss…”
BackgroundMethodInfluentialSelf-citationTranscriptome analysis reveals tumor microenvironment changes in glioblastoma
Hoogstrate, Y., Draaisma, K., Ghisai, S. A., van Hijfte, L., Barin, N., de Heer, I., Coppieters, W., van den Bosch, T. P. P., Bolleboom, A., Gao, Z., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.019 · Cited by 240 · see citing works
Single-nucleus RNA-seq identifies Huntington disease astrocyte states
Al-Dalahmah, O., Sosunov, A. A., Shaik, A., Ofori, K., Liu, Y., Vonsattel, J. P., Adorjan, I., Menon, V., & Goldman, J. E. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-0880-6 · Cited by 264 · see citing works
Mapping gene transcription and neurocognition across human neocortex
Hansen, J. Y., Markello, R. D., Vogel, J. W., Seidlitz, J., Bzdok, D., & Mišić, B. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01082-z · Cited by 203 · see citing works
Lee, S., Devanney, N. A., Golden, L. R., Smith, C. T., Schwartz, J. L., Walsh, A. E., Clarke, H. A., Goulding, D. S., Allenger, E. J., Morillo-Segovia, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112196 · Cited by 203 · see citing works
Ullman, J. C., Arguello, A., Getz, J. A., Bhalla, A., Mahon, C. S., Wang, J., Giese, T., Bedard, C., Kim, D. J., Blumenfeld, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay1163 · Cited by 205 · see citing works
A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain
Del-Aguila, J. L., Li, Z., Dube, U., Mihindukulasuriya, K. A., Budde, J. P., Fernandez, M. V., Ibanez, L., Bradley, J., Wang, F., Bergmann, K., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0524-x · Cited by 183 · see citing works
Wang, M., Li, A., Sekiya, M., Beckmann, N. D., Quan, X., Schrode, N., Fernando, M. B., Yu, A., Zhu, L., Cao, J., et al. (2021) · Neuron · doi:10.1016/j.neuron.2020.11.002 · Cited by 185 · see citing works
Self-citationTrapecar, M., Wogram, E., Svoboda, D., Communal, C., Omer, A., Lungjangwa, T., Sphabmixay, P., Velazquez, J., Schneider, K., Wright, C. W., et al. (2021) · Science Advances · doi:10.1126/sciadv.abd1707 · Cited by 176 · see citing works
Maitra, M., Mitsuhashi, H., Rahimian, R., Chawla, A., Yang, J., Fiori, L. M., Davoli, M. A., Perlman, K., Aouabed, Z., Mash, D. C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38530-5 · Cited by 164 · see citing works
Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains
Khrameeva, E., Kurochkin, I., Han, D., Guijarro, P., Kanton, S., Santel, M., Qian, Z., Rong, S., Mazin, P., Sabirov, M., et al. (2020) · Genome Research · doi:10.1101/gr.256958.119 · Cited by 162 · see citing works
Receptor-mediated transcytosis for brain delivery of therapeutics: receptor classes and criteria
Haqqani, A. S., Bélanger, K., & Stanimirovic, D. B. (2024) · Frontiers in Drug Delivery · doi:10.3389/fddev.2024.1360302 · Cited by 153 · see citing works
A Parkinson's disease CircRNAs Resource reveals a link between circSLC8A1 and oxidative stress
Hanan, M., Simchovitz, A., Yayon, N., Vaknine, S., Cohen‐Fultheim, R., Karmon, M., Madrer, N., Rohrlich, T. M., Maman, M., Bennett, E. R., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201911942 · Cited by 141 · see citing works
Buchholz, S., & Zempel, H. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13784 · Cited by 145 · see citing works
Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease
Serrano-Pozo, A., Li, H., Li, Z., Muñoz-Castro, C., Jaisa-aad, M., Healey, M. A., Welikovitch, L. A., Jayakumar, R., Bryant, A. G., Noori, A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01791-4 · Cited by 140 · see citing works
Seney, M. L., Kim, S.-M., Glausier, J. R., Hildebrand, M. A., Xue, X., Zong, W., Wang, J., Shelton, M. A., Phan, B. N., Srinivasan, C., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.06.007 · Cited by 133 · see citing works
Hanan, M., Simchovitz, A., Yayon, N., Vaknine, S., Cohen‐Fultheim, R., Karmon, M., Madrer, N., Rohrlich, T. M., Maman, M., Bennett, E. R., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202013551 · Cited by 127 · see citing works
SARS-CoV-2 promotes microglial synapse elimination in human brain organoids
Samudyata, S., Oliveira, A. O., Malwade, S., de Sousa, N. R., Goparaju, S. K., Gracias, J., Orhan, F., Steponaviciute, L., Schalling, M., Sheridan, S. D., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01786-2 · Cited by 121 · see citing works
Single cell transcriptome profiling of the human alcohol-dependent brain
Brenner, E., Tiwari, G. R., Kapoor, M., Liu, Y., Brock, A., & Mayfield, R. D. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa038 · Cited by 118 · see citing works
Decoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain
Androvic, P., Kirdajova, D., Tureckova, J., Zucha, D., Rohlova, E., Abaffy, P., Kriska, J., Valny, M., Anderova, M., Kubista, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107777 · Cited by 118 · see citing works
Tau as a Biomarker of Neurodegeneration
Holper, S., Watson, R., & Yassi, N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23137307 · Cited by 114 · see citing works
Elyanow, R., Dumitrascu, B., Engelhardt, B. E., & Raphael, B. J. (2020) · Genome Research · doi:10.1101/gr.251603.119 · Cited by 105 · see citing works
Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
Patrick, E., Taga, M. F., Ergün, A., Ng, B., Casazza, W., Cimpean, M., Yung, C. J., Schneider, J. A., Bennett, D. A., Gaiteri, C., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008120 · Cited by 108 · see citing works
Glioblastoma epigenome profiling identifies SOX10 as a master regulator of molecular tumour subtype
Wu, Y., Fletcher, M., Gu, Z., Wang, Q., Costa, B., Bertoni, A., Man, K.-H., Schlotter, M., Felsberg, J., Mangei, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20225-w · Cited by 101 · see citing works
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works
Self-citationAssociation of a common genetic variant with Parkinson’s disease is mediated by microglia
Langston, R. G., Beilina, A., Reed, X., Kaganovich, A., Singleton, A. B., Blauwendraat, C., Gibbs, J. R., & Cookson, M. R. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abp8869 · Cited by 93 · see citing works
Reid, M. J., Beltran-Lobo, P., Johnson, L., Perez‐Nievas, B. G., & Noble, W. J. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.572850 · Cited by 92 · see citing works
GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis
Kajiwara, Y., Wang, E., Wang, M., Sin, W. C., Brennand, K., Schadt, E. E., Naus, C. C. G., Buxbaum, J. D., & Zhang, B. (2018) · Acta Neuropathologica Communications · doi:10.1186/s40478-018-0642-x · Cited by 93 · see citing works
Self-citationSpatial transcriptomic clocks reveal cell proximity effects in brain ageing
Sun, E., Zhou, O. Y., Hauptschein, M., Rappoport, N., Xu, L., Negredo, P. N., Liu, L. A., Rando, T. A., Zou, J., & Brunet, A. (2024) · Nature · doi:10.1038/s41586-024-08334-8 · Cited by 98 · see citing works
Midbrain organoids with an SNCA gene triplication model key features of synucleinopathy
Mohamed, N., Sirois, J., Ramamurthy, J., Mathur, M., Lépine, P., Deneault, É., Maussion, G., Nicouleau, M., Chen, C. X. ‐., Abdian, N., et al. (2021) · Brain Communications · doi:10.1093/braincomms/fcab223 · Cited by 89 · see citing works
Hartl, C., Ramaswami, G., Pembroke, W. G., Muller, S., Pintacuda, G., Saha, A., Parsana, P., Battle, A. J., Lage, K., & Geschwind, D. H. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00887-5 · Cited by 82 · see citing works
Reemst, K., Kracht, L., Kotah, J. M., Rahimian, R., van Irsen, A. A. S., Sotomayor, G. C., Verboon, L. N., Brouwer, N., Simard, S., Turecki, G., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02265-6 · Cited by 75 · see citing works
Iturria‐Medina, Y., Khan, A. F., Adewale, Q., Shirazi, A. H., & Initiative, T. A. D. N. (2019) · Brain · doi:10.1093/brain/awz400 · Cited by 77 · see citing works
Gleichman, A. J., Kawaguchi, R., Sofroniew, M. V., & Carmichael, S. T. (2023) · Nature Communications · doi:10.1038/s41467-023-42746-w · Cited by 82 · see citing works
Cell-type-specific epigenetic effects of early life stress on the brain
Rahman, M. F., & McGowan, P. O. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02076-9 · Cited by 75 · see citing works
Neurophysiological signatures of cortical micro-architecture
Shafiei, G., Fulcher, B., Voytek, B., Satterthwaite, T. D., Baillet, S., & Mišić, B. (2023) · Nature Communications · doi:10.1038/s41467-023-41689-6 · Cited by 75 · see citing works
Delbridge, A. R. D., Huh, D., Brickelmaier, M., Burns, J. C., Roberts, C., Challa, R., Raymond, N., Cullen, P., Carlile, T. M., Ennis, K. A., et al. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.592005 · Cited by 72 · see citing works
X-chromosome regulation and sex differences in brain anatomy
Raznahan, A., & Distèche, C. M. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.10.024 · Cited by 67 · see citing works
Deciphering cellular transcriptional alterations in Alzheimer’s disease brains
Wang, X., Allen, M., Li, S., Quicksall, Z., Patel, T., Carnwath, T., Reddy, J. S., Carrasquillo, M. M., Lincoln, S., Nguyen, T., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00392-6 · Cited by 68 · see citing works
Brain Organoids as Tools for Modeling Human Neurodevelopmental Disorders
Adams, J. W., Cugola, F. R., & Muotri, A. R. (2019) · Physiology · doi:10.1152/physiol.00005.2019 · Cited by 64 · see citing works
Gupta, C., Chandrashekar, P. B., Jin, T., He, C., Khullar, S., Chang, Q., & Wang, D. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09438-w · Cited by 64 · see citing works
Patel, T., Carnwath, T., Wang, X., Allen, M., Lincoln, S., Lewis‐Tuffin, L. J., Quicksall, Z., Lin, S., Tutor‐New, F. Q., Ho, C. C. G., et al. (2022) · Aging Cell · doi:10.1111/acel.13606 · Cited by 60 · see citing works
Zhao, J., Lü, W., Ren, Y., Fu, Y., Martens, Y. A., Shue, F., Davis, M. D., Wang, X., Chen, K., Li, F., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02361-9 · Cited by 60 · see citing works
Infants’ cortex undergoes microstructural growth coupled with myelination during development
Natu, V. S., Rosenke, M., Wu, H., Querdasi, F. R., Kular, H., Lopez-Alvarez, N., Grotheer, M., Berman, S., Mezer, A., & Kalanit Grill-spector, A. (2021) · Communications Biology · doi:10.1038/s42003-021-02706-w · Cited by 58 · see citing works
Glial mitochondrial function and dysfunction in health and neurodegeneration
McAvoy, K., & Kawamata, H. (2019) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2019.103417 · Cited by 57 · see citing works
Belonwu, S., Li, Y., Bunis, D. G., Rao, A. A., Solsberg, C. W., Tang, A., Fragiadakis, G. K., Dubal, D. B., Oskotsky, T., & Sirota, M. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02591-8 · Cited by 54 · see citing works
Extralysosomal cathepsin B in central nervous system: Mechanisms and therapeutic implications
Ni, J., Lan, F., Xu, Y., Nakanishi, H., & Li, X. (2022) · Brain Pathology · doi:10.1111/bpa.13071 · Cited by 44 · see citing works
The long noncoding RNA FEDORA is a cell type– and sex-specific regulator of depression
Issler, O., van der Zee, Y. Y., Ramakrishnan, A., Xia, S.-H., Zinsmaier, A. K., Tan, C., Li, W., Browne, C. J., Walker, D. M., Salery, M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn9494 · Cited by 53 · see citing works
Browne, C. J., Futamura, R., Minier-Toribio, A. M., Hicks, E. M., Ramakrishnan, A., Martínez-Rivera, F. J., Estill, M. S., Godino, A., Parise, E. M., Torres‐Berrío, A., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg8558 · Cited by 48 · see citing works
Self-citationHoll, D., Hau, W. F., Julien, A., Banitalebi, S., Kalkitsas, J., Savant, S., Llorens-Bobadilla, E., Hérault, Y., Pavlovic, G., Amiry-Moghaddam, M. R., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01678-4 · Cited by 52 · see citing works
Milind, N., Preuß, C., Haber, A., Ananda, G., Mukherjee, S., Cai, J., Shapley, S. M., Tyler, A. L., Logsdon, B. A., Crane, P. K., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008775 · Cited by 50 · see citing works
Consequences and opportunities arising due to sparser single-cell RNA-seq datasets
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2023) · Genome biology · doi:10.1186/s13059-023-02933-w · Cited by 51 · see citing works
Agboola, O. S., Hu, X., Shan, Z., Wu, Y., & Lei, L. (2021) · Stem Cell Research & Therapy · doi:10.1186/s13287-021-02369-8 · Cited by 50 · see citing works
Patel, D., Zhang, X., Farrell, J. J., Chung, J., Stein, T. D., Lunetta, K. L., & Farrer, L. A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01373-z · Cited by 49 · see citing works
McFarland, K. N., & Chakrabarty, P. (2022) · Neurotherapeutics · doi:10.1007/s13311-021-01179-3 · Cited by 48 · see citing works
Day-night and seasonal variation of human gene expression across tissues
Wucher, V., Sodaei, R., Amador, R., Irimia, M., & Guigó, R. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3001986 · Cited by 47 · see citing works
İş, Ö., Wang, X., Reddy, J. S., Min, Y., Yılmaz, E., Bhattarai, P., Patel, T., Bergman, J., Quicksall, Z., Heckman, M. G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48926-6 · Cited by 50 · see citing works
Layer-By-Layer: The Case for 3D Bioprinting Neurons to Create Patient-Specific Epilepsy Models
Antill‐O'Brien, N., Bourke, J., & O’Connell, C. D. (2019) · Materials · doi:10.3390/ma12193218 · Cited by 47 · see citing works
Coninx, E., Chew, Y. C., Yang, X., Guo, W., Coolkens, A., Baatout, S., Moons, L., Verslegers, M., & Quintens, R. (2020) · Aging · doi:10.18632/aging.104056 · Cited by 45 · see citing works
Cell type-specific potential pathogenic genes and functional pathways in Alzheimer’s Disease
Wang, X., & Li, L. (2021) · BMC Neurology · doi:10.1186/s12883-021-02407-1 · Cited by 46 · see citing works
Type I interferon shapes brain distribution and tropism of tick-borne flavivirus
Chotiwan, N., Rosendal, E., Willekens, S. M. A., Schexnaydre, E. E., Nilsson, E., Lindqvist, R., Hahn, M., Mihai, I. S., Morini, F., Zhang, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37698-0 · Cited by 43 · see citing works
Dang, M. T., Gonzalez, M. V., Gaonkar, K. S., Rathi, K. S., Young, P. A., Arif, S., Zhai, L., Alam, Z., Devalaraja, S., To, T. K. J., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108917 · Cited by 44 · see citing works
BarkBase: Epigenomic Annotation of Canine Genomes
Megquier, K., Genereux, D. P., Hekman, J. P., Swofford, R., Turner-Maier, J., Johnson, J., Alonso, J., Li, X., Morrill, K. M., Anguish, L. J., et al. (2019) · Genes · doi:10.3390/genes10060433 · Cited by 40 · see citing works
Huang, Y., Wang, M., Ni, H., Zhang, J., Li, A., Hu, B., Junqueira Alves, C., Wahane, S., Rios de Anda, M., Ho, L., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01664-w · Cited by 42 · see citing works
“78) and the Molecular Signatures Database (MSigDB) 79,80 with Fisher exact test.”
Self-citationCell-type-specific expression of tRNAs in the brain regulates cellular homeostasis
Kapur, M., Molumby, M. J., Guzman, C., Heinz, S., & Ackerman, S. L. (2024) · Neuron · doi:10.1016/j.neuron.2024.01.028 · Cited by 40 · see citing works
Kirmiz, M., Gillies, T. E., Dickson, E. J., & Trimmer, J. S. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.007635 · Cited by 42 · see citing works
Exploring lithium’s transcriptional mechanisms of action in bipolar disorder: a multi-step study
Akkouh, I. A., Skrede, S., Holmgren, A., Ersland, K. M., Hansson, L. J., Bahrami, S., Andreassen, O. A., Steen, V. M., Djurovic, S., & Hughes, T. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0556-8 · Cited by 42 · see citing works
Astrocyte Molecular Clock Function in the Nucleus Accumbens Is Important for Reward-Related Behavior
Becker‐Krail, D. D., Ketchesin, K. D., Burns, J. N., Zong, W., Hildebrand, M. A., DePoy, L. M., Vadnie, C. A., Tseng, G. C., Logan, R. W., Huang, Y. H., et al. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.02.007 · Cited by 42 · see citing works
Forrest, S. L., Lee, S., Nassir, N., Martínez-Valbuena, I., Sackmann, V., Li, J., Ahmed, A., Tartaglia, M. C., Ittner, L. M., Lang, A. E., et al. (2023) · Acta Neuropathologica · doi:10.1007/s00401-023-02604-x · Cited by 44 · see citing works
van Hijfte, L., Geurts, M., Vallentgoed, W., Eilers, P. H. C., Smitt, P. A. E. S., Debets, R., & French, P. J. (2022) · iScience · doi:10.1016/j.isci.2022.105760 · Cited by 41 · see citing works
Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis
Wang, E., Yu, K., Cao, J., Wang, M., Katsel, P., Song, W., Wang, Z., Li, Y., Wang, X., Wang, Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.038 · Cited by 44 · see citing works
Self-citationLi, H., Funk, C. C., McFarland, K. N., Dammer, E. B., Allen, M., Carrasquillo, M. M., Levites, Y., Chakrabarty, P., Burgess, J. D., Wang, X., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12254 · Cited by 41 · see citing works
Mendez, E. F., Wei, H., Hu, R., Stertz, L., Fries, G. R., Wu, X., Najera, K., Monterey, M. D., Lincoln, C. M. M., Kim, J.-W., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01259-y · Cited by 40 · see citing works
Sadeghi, I., Gispert, J. D., Palumbo, E., Muñoz-Aguirre, M., Wucher, V., D’Argenio, V., Santpere, G., Navarro, A., Guigó, R., & Vilor‐Tejedor, N. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.037 · Cited by 39 · see citing works
Chucair‐Elliott, A. J., Ocañas, S. R., Stanford, D. R., Ansere, V. A., Buettner, K. B., Porter, H. L., Eliason, N. L., Reid, J. J., Sharpe, A. L., Stout, M. B., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01418-x · Cited by 37 · see citing works
Aditi, A., Downing, S. M., Schreiner, P. A., Kwak, Y. D., Li, Y., Shaw, T. I., Russell, H. R., & McKinnon, P. J. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.040 · Cited by 40 · see citing works
Multi-omics signatures of alcohol use disorder in the dorsal and ventral striatum
Zillich, L., Poisel, E., Frank, J., Foo, J. C., Friske, M. M., Streit, F., Sirignano, L., Heilmann‐Heimbach, S., Heimbach, A., Hoffmann, P., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01959-1 · Cited by 38 · see citing works
DNA damage response in neurodevelopment and neuromaintenance
Qing, X., Zhang, G., & Wang, Z. (2022) · FEBS Journal · doi:10.1111/febs.16535 · Cited by 38 · see citing works
Heo, G., Xu, Y., Wang, E., Ali, M., Oh, H. S., Losada, P. M., Anastasi, F., Escalante, A. G., Puerta, R., Song, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00872-8 · Cited by 49 · see citing works
Self-citationNassir, N., Bankapur, A., Samara, B., Ali, A. Y., Ahmed, A., Inuwa, I. M., Zarrei, M., Shabestari, S. A. S., Albanna, A., Howe, J., et al. (2021) · Human Genomics · doi:10.1186/s40246-021-00368-7 · Cited by 38 · see citing works
Rayan, N. A., Kumar, V., Aow, J., Rastegar, N., Lim, M. G. L., O’Toole, N., Aliwarga, E., Arcego, D. M., Yeo, G. H. T., Wong, J. Y., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01725-1 · Cited by 37 · see citing works
Tumor heterogeneity and tumor-microglia interactions in primary and recurrent IDH1-mutant gliomas
Blanco-Carmona, E., Narayanan, A., Hernandez, I., Nieto, J. C., Elosua-Bayés, M., Sun, X., Schmidt, C. S., Pamir, M. N., Özduman, K., Herold‐Mende, C. C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101249 · Cited by 37 · see citing works
Haghani, A., Cacciottolo, M., Doty, K. R., D’Agostino, C., Thorwald, M. A., Safi, N., Levine, M. E., Sioutas, C., Town, T., Forman, H. J., et al. (2020) · eLife · doi:10.7554/elife.54822 · Cited by 37 · see citing works
Lam, I., Ndayisaba, A., Lewis, A. J., Fu, Y., Sagredo, G. T., Kuzkina, A., Zaccagnini, L., Celikag, M., Sandoe, J., Sanz, R. L., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.06.002 · Cited by 35 · see citing works
Zhou, Z., Bai, J., Zhong, S., Zhang, R., Kang, K., Zhang, X., Xu, Y., Zhao, C., & Zhao, M. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/5555634 · Cited by 36 · see citing works
Promoter DNA hypermethylation – Implications for Alzheimer’s disease
Liu, Y., Wang, M., Marcora, E. M., Zhang, B., & Goate, A. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2019.134403 · Cited by 35 · see citing works
Self-citationCastelli, L. M., Cutillo, L., Souza, C. D. S., Sánchez-Martínez, Á., Granata, I., Lin, Y.-H., Myszczynska, M. A., Heath, P. R., Livesey, M. R., Ning, K., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00475-y · Cited by 33 · see citing works
Chiou, K. L., DeCasien, A. R., Rees, K. P., Testard, C., Spurrell, C. H., Gogate, A. A., Pliner, H. A., Tremblay, S., Mercer, A., Whalen, C. J., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01197-0 · Cited by 35 · see citing works
Rompala, G. R., Nagamatsu, S. T., Martínez‐Magaña, J. J., Núñez-Ríos, D. L., Wang, J., Girgenti, M. J., Krystal, J. H., Gelernter, J. E., Alvarez, V. E., Benedek, D. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40285-y · Cited by 33 · see citing works
Self-citationGenes associated with cognitive performance in the Morris water maze: an RNA-seq study
Reshetnikov, V. V., Kisaretova, P. E., Ershov, N., Shulyupova, A. S., Oshchepkov, D. Y., Klimova, N. V., Ivanchikhina, A. V., Merkulova, T. I., & Bondar, N. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-78997-6 · Cited by 32 · see citing works
Identification of cell-type-specific marker genes from co-expression patterns in tissue samples
Qiu, Y., Wang, J., Lei, J., & Roeder, K. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab257 · Cited by 32 · see citing works
Reich, M., Simon, M. J., Polke, B., Paris, I., Werner, G., Schrader, C. M., Spieth, L., Davis, S. S., Robinson, S., de Melo, G. L., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adj7308 · Cited by 31 · see citing works
Pathak, G. A., Singh, K., Wendt, F. R., Miller‐Fleming, T. W., Overstreet, C. M., Koller, D., Tylee, D. S., Angelis, F. D., Cabrera‐Mendoza, B., Levey, D. F., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01488-9 · Cited by 28 · see citing works
- Authors
- McKenzie, A. T., Wang, M., Hauberg, M. E., Fullard, J. F., Kozlenkov, A., Keenan, A., Hurd, Y. L., Dracheva, S., Casaccia, P., Roussos, P., & Zhang, B.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Aarhus University (DK)
- James J. Peters VA Medical Center (US)
- City University of New York (US)
- Corresponding author
- Zhang, Bin
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-06-11
- Volume
- 8
- Issue
- 1
- Pages
- 8868-8868
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5995803
- OpenAlex
- W2806989085
- Cited by
- 481
- References
- 59
- Citations by year
- 2026: 41 · 2025: 59 · 2024: 84 · 2023: 70 · 2022: 60 · 2021: 78 · 2020: 56 · 2019: 28 · 2018: 5
- FWCI
- 15.03
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-09-24)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Scopus (Elsevier) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (I01 BX002876, I01BX002395, I01 BX003625)
- Lundbeckfonden (R155-2014-1724, R248‐2017‐2003)
- National Institutes of Health
- National Institute on Aging (R01AG057907, F30AG052261, R01AG046170, U01AG046170, RF1AG054014, RF1AG057440, U01AG052411, R01AG050986)
- National Institute of Mental Health (R01MH109677)
- National Institute on Drug Abuse (P01DA008227)
- National Institute of Neurological Disorders and Stroke (R37NS042925, R01NS052738)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- 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
- Keywords
- cell type markers, co-expression network, single-cell transcriptomics, cell type deconvolution, cell type signatures, intercellular communication, postmortem brain
- MeSH terms
- Animals, Female, Genetic Markers, Humans, Male, Mice, Inbred C57BL, Neuroglia, Neurons, Temporal Lobe, Gene Expression Profiling, Databases, Nucleic Acid, Endothelial Cells, Mice, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome, Datasets as Topic
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
