Cited by 202Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Chromatin Dynamics

The DOI matches its Crossref record; 107 of the 109 references it cites were found in a registry or an index.

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

Open access

Kelley, Kevin W., Nakao-Inoue, Hiromi, Molofsky, Anna V., Oldham, Michael C.·2018·Nature Neuroscience

DOI 10.1038/s41593-018-0216-zPMC PMC6192711

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

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

Crossref · 10.1038/s41593-018-0216-z

Cites

107 of 107 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
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
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328

Show all 109 references

Sorted by citations

Items 101–200 of 202

Human myelin protein P2: from crystallography to time‐lapse membrane imaging and neuropathy‐associated variants
Uusitalo, M., Klenow, M. B., Laulumaa, S., Blakeley, M. P., Simonsen, A. C., Ruskamo, S., & Kursula, P. (2021) · FEBS Journal · doi:10.1111/febs.16079 · Cited by 17
A human tau seeded neuronal cell model recapitulates molecular responses associated with Alzheimer’s disease
Ficulle, E., Kananathan, S., Airey, D., Gharbi, S., Humphryes-Kirilov, N., Scherschel, J., Dunbar, C., Eastwood, B. J., Laing, E., Collier, D. A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-06411-4 · Cited by 18
Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue
Chameh, H. M., Falby, M. R., Movahed, M., Arbabi, K., Rich, S., Zhang, L., Lefebvre, J., Tripathy, S. J., Pittà, M. D., & Valiante, T. A. (2023) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2023.1250834 · Cited by 15
Bulk and Mosaic Deletions of Egfr Reveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain
Zhang, X., Xiao, G., Johnson, C. A., Cai, Y., Horowitz, Z. K., Mennicke, C. V., Coffey, R. J., Haider, M. A., Threadgill, D. W., Eliscu, R., et al. (2022) · iScience · doi:10.1016/j.isci.2023.106242 · Cited by 14
Self-citation
Brain Proteome Profiling Reveals Common and Divergent Signatures in Parkinson’s Disease, Multiple System Atrophy, and Progressive Supranuclear Palsy
Dick, F., Johanson, G. A. S., Tysnes, O., Alves, G. W., Dölle, C., & Tzoulis, C. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04422-y · Cited by 13
Structural brain preservation: a potential bridge to future medical technologies
McKenzie, A. T., Zeleznikow-Johnston, A., Sparks, J. S., Nnadi, O., Smart, J., Wiley, K., Cerullo, M. A., de Wolf, A., Minerva, F., Risco, R., et al. (2024) · Frontiers in Medical Technology · doi:10.3389/fmedt.2024.1400615 · Cited by 15
Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury
Huie, J. R., Ferguson, A. R., Kyritsis, N., Pan, J. Z., Irvine, K., Nielson, J. L., Schupp, P. G., Oldham, M. C., Gensel, J. C., Lin, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-82951-5 · Cited by 13
Self-citation
Generation of functional human oligodendrocytes from dermal fibroblasts by direct lineage conversion
Tanabe, K., Nobuta, H., Yang, N., Ang, C. E., Huie, P., Jordan, S., Oldham, M. C., Rowitch, D. H., & Wernig, M. (2022) · Development · doi:10.1242/dev.199723 · Cited by 13
Self-citation
Functional Genomics of Epileptogenesis in Animal Models and Humans
Forero, D. A. (2020) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-020-00927-x · Cited by 12
A porcine brain-wide RNA editing landscape
Huang, J., Lin, L., Dong, Z., Yang, L. M., Zheng, T., Gu, W., Zhang, Y., Yin, T., Sjöstedt, E., Mulder, J., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02238-3 · Cited by 13
Unraveling Targetable Systemic and Cell-Type-Specific Molecular Phenotypes of Alzheimer’s and Parkinson’s Brains With Digital Cytometry
Bordone, M. C., & Barbosa‐Morais, N. L. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.607215 · Cited by 9
Not every estimate counts – evaluation of cell composition estimation approaches in brain bulk tissue data
Toker, L., Nido, G. S., & Tzoulis, C. (2023) · Genome Medicine · doi:10.1186/s13073-023-01195-2 · Cited by 10
Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony
de Thé, F.-X. B., Baudier, C., Pereira, R. A., Lefèbvre, C., & Moingeon, P. E. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103772 · Cited by 12
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) · doi:10.1101/593756 · Cited by 12
Regional and aging-specific cellular architecture of non-human primate brains
Wang, Y.-M., Wang, W., Pan, Y., Zeng, L., Wu, J., Wang, Z., Zhuang, X.-L., Li, M., Cooper, D. N., Wang, S., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01469-x · Cited by 10
scRNA-Seq of Cultured Human Amniotic Fluid from Fetuses with Spina Bifida Reveals the Origin and Heterogeneity of the Cellular Content
Dasargyri, A., Rodríguez, D. G., Rehrauer, H., Ernst Reichmann, A., Biedermann, T., & Moehrlen, U. (2023) · Cells · doi:10.3390/cells12121577 · Cited by 10
Functional genomics and systems biology in human neuroscience
Konopka, G., & Bhaduri, A. (2023) · Nature · doi:10.1038/s41586-023-06686-1 · Cited by 13
Disease-specific selective vulnerability and neuroimmune pathways in dementia revealed by single cell genomicsPreprint
Rexach, J. E., Cheng, Y., Chen, L., Polioudakis, D., Lin, L., Mitri, V., Elkins, A. G., Yin, A., Calini, D., Kawaguchi, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.29.560245 · Cited by 10
Generation of Mammalian Astrocyte Functional HeterogeneityPreprint
Bartels, T., Rowitch, D. H., & Bayraktar, O. A. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041351 · Cited by 11
Brain Cell Type-Specific Nuclear Proteomics Is Imperative to Resolve Neurodegenerative Disease Mechanisms
Nelson, R. S., Dammer, E. B., Santiago, J. V., Seyfried, N. T., & Rangaraju, S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.902146 · Cited by 10
Origins of atrophy in Parkinson linked to early onset and local transcription patterns
Maia, P. D., Pandya, S., Freeze, B. S., Torok, J., Gupta, A. K., Zeighami, Y., & Raj, A. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa065 · Cited by 9
Transcriptional Cartography Integrates Multiscale Biology of the Human CortexPreprint
Wagstyl, K., Adler, S., Seidlitz, J., Vandekar, S., Mallard, T. T., Dear, R., DeCasien, A. R., Satterthwaite, T. D., Liu, S., Vértes, P. E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.13.495984 · Cited by 9
Twin-peak temporal regulation during human neocortical development
Wang, W., & Wang, G. (2019) · Cell Discovery · doi:10.1038/s41421-019-0129-3 · Cited by 8
Mechanisms of astrocyte development
Sardar, D., Cheng, Y.-T., Szewczyk, Ł. M., Deneen, B., & Molofsky, A. V. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814405-3.00032-1 · Cited by 8
Self-citation
Enzyme therapy of cancer, future studies.
Holcenberg, J. S. (1981) · PubMed · Cited by 7
The architecture of brain co-expression reveals the brain-wide basis of disease susceptibilityPreprint
Hartl, C., Ramaswami, G., Pembroke, W. G., Muller, S., Pintacuda, G., Saha, A., Parsana, P., Battle, A. J., Lage, K., & Geschwind, D. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.05.965749 · Cited by 7
Cell type-specific inference from bulk RNA-sequencing data by integrating single-cell reference profiles via EPIC-unmix
Tang, C., Sun, Q., Zeng, X., Li, G., Yang, X., Liu, F., Zhao, J., Shen, Y., Liu, B., Wen, J., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03847-5 · Cited by 6
Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections
Schupp, P. G., Shelton, S. J., Brody, D. J., Eliscu, R., Johnson, B., Mazor, T., Kelley, K. W., Potts, M. B., McDermott, M. W., Huang, E. J., et al. (2024) · Cancers · doi:10.3390/cancers16132429 · Cited by 5
Self-citation
Isolation of ferret astrocytes reveals their morphological, transcriptional, and functional differences from mouse astrocytes
Roboon, J., Hattori, T., Nguyen, D. T., Ishii, H., Takarada‐Iemata, M., Kannon, T., Hosomichi, K., Maejima, T., Saito, K., Shinmyo, Y., et al. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.877131 · Cited by 5
BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2023) · Genome biology · doi:10.1186/s13059-023-03007-7 · Cited by 5
Sequencing Diversity One Cell at a Time
Oldham, M. C., & Kreitzer, A. C. (2018) · Cell · doi:10.1016/j.cell.2018.07.024 · Cited by 5
Self-citation
Cell type heterogeneity in gene co-expression networks: implications for toxicological research
Bruns, I. B., Li, Y., Stevens, J. L., van de Water, B., & Callegaro, G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf421 · Cited by 5
Taking Cellular Heterogeneity Into Consideration When Modeling Astrocyte Involvement in Amyotrophic Lateral Sclerosis Using Human Induced Pluripotent Stem Cells
Stifani, S. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.707861 · Cited by 4
Tweaking neural organoids to model human reactive astrocytes
Bezzi, P. (2022) · The Journal of Cell Biology · doi:10.1083/jcb.202202026 · Cited by 4
Neuronal loss drives differentially expressed protein‐pathways in the PSP globus pallidus
Dick, F., Johanson, G. A. S., & Tzoulis, C. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1280 · Cited by 4
Early nuclear phenotypes and reactive transformation in human iPSC ‐derived astrocytes from ALS patients with SOD1 mutations
Soubannier, V., Chaineau, M., Gursu, L., Lépine, S., Kalaydjian, D., Sirois, J., Haghi, G., Rouleau, G. A., Durcan, T. M., & Stifani, S. (2024) · Glia · doi:10.1002/glia.24598 · Cited by 4
Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expressionPreprint
Wang, J., Devlin, B., & Roeder, K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/379099 · Cited by 4
Individual variation in preference behavior in sailfin fish refines the neurotranscriptomic pathway for mate preference
Young, R. L., Price, S. M., Schumer, M. E., Wang, S., & Cummings, M. E. (2023) · Ecology and Evolution · doi:10.1002/ece3.10323 · Cited by 2
Identification and Prioritization of PET Neuroimaging Targets for Microglial Phenotypes Associated with Microglial Activity in Alzheimer’s Disease
Bartolo, N. D., Mortimer, N., Manter, M. A., Sanchez, N., Riley, M. M., O’Malley, T. T., & Hooker, J. M. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00607 · Cited by 3
Dopa decarboxylase is a genetic hub of parental control over offspring behaviorPreprint
Bonthuis, P. J., Steinwand, S., Huang, W.-C., Hörndli, C. S., Emery, J., Kravitz, S. N., Ferris, E., & Gregg, C. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.23.168195 · Cited by 2
voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissuesPreprint
Schneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.22.521681 · Cited by 3
Limits to the inference of gene regulation from bulk tissue expression dataPreprint
Chu, E. C.-P., Morin, A. M., & Pavlidis, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.24.619521 · Cited by 3
NF-κB Involvement in Glaucoma-Associated Neuroinflammation: Focus on Glial Cells
Veglianti, F., Nolfi, M. D. V., Flati, I., Dall’Aglio, F., Giovanni, F. D., Verzella, D., Capece, D., Angelucci, A., Alesse, E., Zazzeroni, F., et al. (2026) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl45644 · Cited by 3
Astrocyte TrkB.T1 Deficiency Disrupts Glutamatergic Synaptogenesis and Astrocyte–Synapse Interactions
Pinkston, B. T. C., Browning, J. L., Larson, A. P., & Olsen, M. L. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.2002-24.2026 · Cited by 3
Single-cell RNA sequencing reveals anterograde trans-synaptic degeneration and exacerbated synaptic remodeling in myopia
Zhang, R., Xie, Y., Zhang, M., Li, Z., Guo, L., Wei, H., Li, X., Xin, J., Yang, Z., Wen, Y., et al. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01489-y · Cited by 5
A Human Neuron/Astrocyte Co‐culture to Model Seeded and Spontaneous Intraneuronal Tau Aggregation
Batenburg, K. L., Rohde, S. K., Cornelissen‐Steijger, P., Breeuwsma, N., Heine, V. M., & Scheper, W. (2023) · Current Protocols · doi:10.1002/cpz1.900 · Cited by 2
Extracting new insights from bulk transcriptomics
Menon, V. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0218-x · Cited by 2
Molecular Profiling to Infer Neuronal Cell Identity: Lessons from small ganglia of the Crab Cancer borealisPreprint
Northcutt, A. J., Kick, D. R., Otopalik, A. G., Goetz, B. M., Harris, R. M., Santin, J. M., Hofmann, H. A., Marder, E. E., & Schulz, D. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/690388 · Cited by 2
Disruption of Glial Ca 2+ Oscillations at the Drosophila Blood-Brain Barrier Predisposes to Seizure-Like BehaviorPreprint
Weiss, S., Clamon, L. C., Manoim, J. E., Ormerod, K. G., Parnas, M., & Littleton, J. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.16.285841 · Cited by 2
Transcriptional Cartography Integrates Multiscale Biology of the Human CortexPreprint
Wagstyl, K., Adler, S., Seidlitz, J., Vandekar, S., Mallard, T. T., Dear, R., DeCasien, A. R., Satterthwaite, T. D., Liu, S., Vértes, P. E., et al. (2023) · eLife · doi:10.7554/elife.86933.1 · Cited by 2
Bioinformatic Analysis of Epigenomic Studies for Major Depressive Disorder
Alam, F. B., González‐Giraldo, Y., & Forero, D. A. (2022) · Archivos de Neurociencias · doi:10.31157/an.v27i2.326 · Cited by 1
Beyond Rodents and Primates: Uncovering Cortical Astrocyte Diversity across Mammals
Pistorio, G., & Falcone, C. (2025) · Brain Behavior and Evolution · doi:10.1159/000546178 · Cited by 1
Addressing neuroinflammation in human induced pluripotent stem cell-derived central nervous system neurospheroids
Gomes, C. M., Simão, D. F., Gonçalves, C. C., Silva, G. P. D. L., Painho, B., Alves, P. M., & Brito, C. (2025) · iScience · doi:10.1016/j.isci.2025.113246 · Cited by 1
Novel Roles for the Ectoenzyme CD38 in the Maintenance of Transcriptional and Metabolic Homeostasis in Astrocytes
Basak, S., Colafrancesco, A. M., Hernandez, R. D., Wei, X., McMeekin, L. J., Dang, D., Simmons, M., Browning, J. L., Noel, K., Zhou, F., et al. (2025) · Glia · doi:10.1002/glia.70112 · Cited by 1
Human Peripheral Myelin Protein 2 and Charcot–Marie–Tooth Disease or Structural Missense Variants Show Different Binding to Myelin‐Like Lipid Monolayers
Schöffmann, F. A., Imran, M. A. S., Schwieger, C., Hetland, Ø., Jerschabek, V., Raasakka, A., Kursula, P., & Hinderberger, D. (2026) · ChemBioChem · doi:10.1002/cbic.202500947 · Cited by 1
Does SIGLEC8 localize to the subcellular compartment like the Alzheimer's disease protective CD33 splice variant?
Dhar, C. (2023) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2023.1124150 · Cited by 1
Neurology and Psychiatry Get Closer Again: Lessons From Dystonia
Erro, R. (2020) · Movement Disorders · doi:10.1002/mds.28414 · Cited by 1
Bulk and Mosaic Deletions of Egfr Reveal Regionally Defined Gliogenesis in the Developing Mouse ForebrainPreprint
Zhang, X., Xiao, G., Johnson, C. A., Cai, Y., Mennicke, C. V., Coffey, R. J., Haider, M. A., Threadgill, D. W., Eliscu, R., Oldham, M. C., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4141622 · Cited by 1
Self-citation
A Robust Bayesian Approach to Bulk Gene Expression Deconvolution with Noisy Reference SignaturesPreprint
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.25.513800 · Cited by 1
Early nuclear phenotypes and reactive transformation in human iPSC-derived astrocytes from ALS patients with SOD1 mutationsPreprint
Soubannier, V., Chaineau, M., Gursu, L., Lépine, S., Kalaydjian, D., Haghi, G., Rouleau, G. A., Durcan, T. M., & Stifani, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.05.561079 · Cited by 1
Navigating the Molecular Maze of Alzheimer’s Disease: Developing A Human Hippocampal Atlas to Bridge Discovery and Clinical PracticePreprint
Wang, P., Wang, L., Tao, Q., Guo, Z., Luo, T., Xu, Z. P., He, Y., Yu, J., Liu, Y., Wu, Z., et al. (2023) · medRxiv · doi:10.1101/2023.12.06.23299593 · Cited by 1
Transcriptional Cartography Integrates Multiscale Biology of the Human CortexPreprint
Wagstyl, K., Adler, S., Seidlitz, J., Vandekar, S., Mallard, T. T., Dear, R., DeCasien, A. R., Satterthwaite, T. D., Liu, S., Vértes, P. E., et al. (2024) · eLife · doi:10.7554/elife.86933.2 · Cited by 1
A network-based atlas of human skeletal muscle agingPreprint
Stokes, T., Lim, C., Ali, M., Mcleod, J. C., Gisby, J., Mariniello, K., Crossland, H., Sharif, J.-A., Deane, C. S., Moseley, T., et al. (2026) · medRxiv · doi:10.64898/2026.02.15.26346348 · Cited by 1
Fatty acid binding protein-8 (FABP8/PMP2) reveals molecular heterogeneity of myelin sheaths in the human CNSPreprint
Gargareta, V., Mougios, N., Hahn, E. T., Siems, S. B., Crisp, S. J., Varga, B. V., Káradóttir, R. T., Buescher, J. M., Jung, R. B., Ramesh, V., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.09.730898 · Cited by 1
Persistence of Drug Memories: Melting Transcriptomes
Mayfield, R. D., & Harris, R. A. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.09.023
The molecular underpinnings of neuronal cell identity in the stomatogastric ganglion of cancer borealis
Northcutt, A. J. (2019) · doi:10.32469/10355/69970
Coupling, membrane conductance, and ion channel mRNA profiles in the establishment and maintenance of network activity in the crustacean cardiac ganglion
Kick, D. R. (2021) · doi:10.32469/10355/85790
Faculty Opinions recommendation of Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer.
Mercurio, A. M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736702009.793569052
Faculty Opinions recommendation of Hyperactivity with Disrupted Attention by Activation of an Astrocyte Synaptogenic Cue.
Goda, Y., & Chater, T. E. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735606871.793560276
Faculty Opinions recommendation of Comprehensive functional genomic resource and integrative model for the human brain.
Nürnberger, J. I. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734622872.793563012
Reviewer #2 (Public Review): Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex
Wagstyl, K., Adler, S., Jakob, S., et al. (2023) · eLife · doi:10.7554/elife.86933.1.sa1
eLife Assessment: Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex
Jbabdi, S. (2023) · eLife · doi:10.7554/elife.86933.1.sa3
Reviewer #1 (Public Review): Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex
Wagstyl, K., Adler, S., Jakob, S., et al. (2023) · eLife · doi:10.7554/elife.86933.1.sa2
Author Response: Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex
Wagstyl, K., Adler, S., Seidlitz, J., Vandekar, S., Mallard, T. T., Dear, R., DeCasien, A. R., Satterthwaite, T. D., Liu, S., Vértes, P. E., et al. (2023) · eLife · doi:10.7554/elife.86933.1.sa0
voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissuesPreprint
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1
Reviewer #2 (Public Review): voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa1
Reviewer #1 (Public Review): voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues
Schneider, A. L., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2023) · eLife · doi:10.7554/elife.88623.1.sa2
eLife Assessment: voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues
Benayoun, B. A. (2023) · eLife · doi:10.7554/elife.88623.1.sa3
Reviewer #3 (Public Review): voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues
Schneider Arthur, L., Nuno, S.-A., & Barbosa-Morais Nuno, L. (2023) · eLife · doi:10.7554/elife.88623.1.sa0
voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissuesPreprint
Schneider, A. L., Martins-Silva, R., Kaizeler, A., Saraiva-Agostinho, N., & Barbosa‐Morais, N. L. (2024) · eLife · doi:10.7554/elife.88623.2
Obsessive-Compulsive Disorder
Osman, H. I. (2024) · International Journal of Research Publications · doi:10.47119/ijrp1001501620246668
Differential expression and modulation of presenilin-1 and presenilin-2 in neural cell linesPreprint
Eccles, M. K., Woodfield, A., Armstrong, A. M., Agostino, M., Groth, D., & Verdile, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.15.618376
Multi-Scale Characterization of Distance-dependent Functional Dysconnections in Major Depressive Disorder Across Two Asian Cohorts: A Genomic, Neurochemical, and Cellular PerspectivePreprint
Qian, R., Gao, H., Qiu, B., Li, Z., Gu, B., Ye, T., Wu, D., & Zhao, Z. (2024) · medRxiv · doi:10.1101/2024.11.20.24317665 · Cited by 1
Methods for In Vivo Circuit Analysis
Johnson, M., Filali, Y., Velamuri, R., Dzirasa, K., & Hultman, R. C. (2025) · doi:10.1093/med/9780197640654.003.0007
Cross-cellular analysis of chromatin accessibility markers H3K4me3 and DNase in the context of detecting cell-identity genes: An “all-or-nothing” approach
Low, B. H., Kaslikar, K. K., Perna, S., & Lee, B. T. K. (2025) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720025400025
Additional file 4 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608368
Additional file 2 of Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition
Nido, G. S., Dick, F., Toker, L., Petersen, K., Alves, G. W., Tysnes, O.-B., Jonassen, I., Haugarvoll, K., & Tzoulis, C. (2020) · Figshare · doi:10.6084/m9.figshare.12171933
Additional file 6 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608374
Additional file 1 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608359
Additional file 7 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608377
Additional file 1 of Evolutionary conservation and divergence of the human brain transcriptome
Pembroke, W. G., Hartl, C., & Geschwind, D. H. (2021) · Figshare · doi:10.6084/m9.figshare.13667952
Additional file 1 of Not every estimate counts – evaluation of cell composition estimation approaches in brain bulk tissue data
Toker, L., Nido, G. S., & Tzoulis, C. (2024) · Figshare · doi:10.6084/m9.figshare.26595188
Additional file 8 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608380
Additional file 2 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608362
Additional file 5 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608371
Additional file 3 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
Ghaffari, S., Bouchonville, K. J., Saleh, E., Schmidt, R. E., Offer, S. M., & Sinha, S. (2024) · Figshare · doi:10.6084/m9.figshare.26608365
Additional file 2 of Not every estimate counts – evaluation of cell composition estimation approaches in brain bulk tissue data
Toker, L., Nido, G. S., & Tzoulis, C. (2024) · Figshare · doi:10.6084/m9.figshare.26595194
Additional file 13 of Evolutionary conservation and divergence of the human brain transcriptome
Pembroke, W. G., Hartl, C., & Geschwind, D. H. (2021) · Figshare · doi:10.6084/m9.figshare.13667949
Single-cell sequencing reveals reversible glial remodeling in the visual cortex during visual deprivation and recovery
Gong, X., Feng, J., Xu, Z., Xie, Y., Han, Y., Li, J., Tang, G., Liu, Y., Dong, X., Li, S., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1730619
Establishment and validation of the NEX-RiboTag system for profiling the excitatory neuronal translatome in the postnatal mouse forebrain
Zheng, K., Liu, M., Jin, B., Chai, G., Wang, S., Yang, Y., Li, K., Huang, H., Lu, C., Zhang, X., et al. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.06.027

Authors: Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V., & Oldham, M. C.
Affiliations: University of California, San Francisco (US)
Corresponding author: Oldham, Michael C.
ORCID: Kelley, Kevin W.
Nakao-Inoue, Hiromi
Molofsky, Anna V.
Oldham, Michael C.
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2018-08-28
Volume: 21
Issue: 9
Pages: 1171-1184
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/s41593-018-0216-z
PMC: PMC6192711
OpenAlex: W2951890520
Cited by: 198
References: 109
Citations by year: 2026: 10 · 2025: 12 · 2024: 22 · 2023: 30 · 2022: 26 · 2021: 26 · 2020: 35 · 2019: 29 · 2018: 7
FWCI: 8.62
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc-nd
www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: Sandler Foundation
University of California, San Francisco
National Institute of Mental Health (K08MH104417, R01MH113896, DP2MH116507)
National Institute of General Medical Sciences (T32GM007618)
Weill Institute for Neurosciences, University of California, San Francisco
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 › Genomics and Chromatin Dynamics
Keywords: astrocytes, Alzheimer's disease, regional brain differences, cross-species comparison, gene expression deconvolution, cell identity
MeSH terms: Alzheimer Disease, Animals, Astrocytes, Brain, Central Nervous System, Humans, Models, Genetic, Transcription, Genetic, Sequence Analysis, RNA, Myelin P2 Protein, Gene Expression Profiling, Databases, Genetic, Cell Size, Mice, Transcriptome

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

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks