A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Open accessPublished version of a preprint
Abstract
The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.
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?
While transmitter-related gene expression and immunoreactivity are largely exclusive between the fast-acting transmitters in flies, most neuropeptides and monoamines can co-transmit with fast-acting transmitters.
“While transmitter-related gene expression and immunoreactivity are largely exclusive between the fast-acting transmitters in flies,21,22,30,31 most neuropeptides and monoamines can co-transmit with fast-acting transmitters.21,163”
From Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster (2024) · cites this paper as [22]
The Results sections on adult-brain cell types and regulatory networks identify distinct cholinergic, glutamatergic, and GABAergic groups, and the rare-cell-types section says neuropeptides are expressed in some other cell types. These passages support parts of the gene-expression account, but they do not establish immunoreactivity, show that most neuropeptides or monoamines co-transmit with fast-acting transmitters, or measure co-transmission itself. No page numbers are available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOur results are conceptually consistent with previous reports of large transcriptional changes with age in some cell identities and more subtle changes in others.
“Our results are conceptually consistent with previous reports of large transcriptional changes with age in some cell identities and more subtle changes in others (Kowalczyk et al. 2015; Keyes et al. 2016; Davie et al. 2018; Keyes and Fuchs 2018).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Davie et al. 2018
Transcriptomic atlases of the central adult brain and optic lobes are appearing.
“Transcriptomic atlases of the central adult brain95,132 and optic lobes133,134 are appearing.”
From Neuronal wiring diagram of an adult brain (2023) · cites this paper as [132]
Aging dramatically reduces amount of RNA in Drosophila brain cells.
“Aging dramatically reduces the amount of RNA in Drosophila brain cells (Davie et al., 2018).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Davie et al., 2018
Metabolic processes, like oxygen consumption and mitochondrial function, are broadly modulated to keep pace with both development and plasticity, consistent with prior reports in neurodevelopment.
“Also, metabolic processes, like oxygen consumption and mitochondrial function are broadly modulated to keep pace with both development and plasticity, consistent with prior reports in neurodevelopment28 (Fig. 4e).”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [28]
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
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890 · see citing works
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · 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
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · 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
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
Self-citationGOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-48 · Cited by 3,356 · 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
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177459 · Cited by 2,740 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131 · see citing works
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · see citing works
GOplot: an R package for visually combining expression data with functional analysis
Walter, W., Sánchez‐Cabo, F., & Ricote, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv300 · Cited by 1,760 · 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
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
Buchbesprechungen / Book Reviews
Förstl, H. (2006) · Zeitschrift für Psychiatrie Psychologie und Psychotherapie · doi:10.1024/1661-4747.54.1.68a · Cited by 1,307 · see citing works
Brogiolo, W., Stocker, H., Ikeya, T., Rintelen, F., Fernandez, R., & Hafen, E. (2001) · Current Biology · doi:10.1016/s0960-9822(01)00068-9 · Cited by 1,255 · see citing works
Ageing, neurodegeneration and brain rejuvenation
Wyss-Coray, T. (2016) · Nature · doi:10.1038/nature20411 · Cited by 1,147 · see citing works
Ablation of Insulin-Producing Neurons in Flies: Growth and Diabetic Phenotypes
Rulifson, E. J., Kim, S. K., & Nusse, R. (2002) · Science · doi:10.1126/science.1070058 · Cited by 1,064 · see citing works
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · see citing works
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900 · see citing works
Self-citation<i>destiny</i> : diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M., Theis, F. J., Marr, C., & Buettner, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv715 · Cited by 640 · see citing works
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Raj, B., Wagner, D. E., McKenna, A., Pandey, S., Klein, A. M., Shendure, J., Gagnon, J. A., & Schier, A. F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4103 · Cited by 622 · 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
Enge, M., Arda, H. E., Mignardi, M., Beausang, J., Bottino, R., Kim, S. K., & Quake, S. R. (2017) · Cell · doi:10.1016/j.cell.2017.09.004 · Cited by 568 · see citing works
FlyBase at 25: looking to the future
Gramates, L. S., Marygold, S. J., dos Santos, G., Urbano, J.-M., Antonazzo, G., Matthews, B. B., Rey, A. J., Tabone, C. J., Crosby, M. A., Emmert, D. B., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1016 · Cited by 535 · see citing works
Differential regulation of circadian pacemaker output by separate clock genes in Drosophila
Park, J. H., Helfrich‐Förster, C., Lee, G., et al. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.7.3608 · Cited by 533 · see citing works
Circadian Organization of Behavior and Physiology in Drosophila
Allada, R., & Chung, B. Y. (2010) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021909-135815 · Cited by 456 · see citing works
repo encodes a glial-specific homeo domain protein required in the Drosophila nervous system.Preprint
Xiong, W., Okano, H., Patel, N. H., Blendy, J. A., & Montell, C. (1994) · Genes & Development · doi:10.1101/gad.8.8.981 · Cited by 425 · see citing works
Major Shifts in Glial Regional Identity Are a Transcriptional Hallmark of Human Brain Aging
Soreq, L., Rose, J., Soreq, E., Hardy, J., Trabzuni, D., Cookson, M. R., Smith, C., Ryten, M., Patani, R., & Ule, J. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.011 · Cited by 419 · see citing works
Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts714 · Cited by 395 · see citing works
The Drosophila embryo at single-cell transcriptome resolution
Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 371 · see citing works
Ensheathing Glia Function as Phagocytes in the Adult Drosophila Brain
Doherty, J., Logan, M. A., Tasdemir-Yilmaz, O. E., & Freeman, M. R. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5951-08.2009 · Cited by 369 · see citing works
Maurange, C., Cheng, L. Y., & Gould, A. P. (2008) · Cell · doi:10.1016/j.cell.2008.03.034 · Cited by 357 · see citing works
Aging increases cell-to-cell transcriptional variability upon immune stimulation
Martinez-Jimenez, C. P., Eling, N., Chen, H.-C., Vallejos, C. A., Kolodziejczyk, A. A., Connor, F., Stojic, L., Rayner, T. F., Stubbington, M. J. T., Teichmann, S. A., et al. (2017) · Science · doi:10.1126/science.aah4115 · Cited by 339 · see citing works
Montell, C., & Rubin, G. M. (1988) · Cell · doi:10.1016/0092-8674(88)90413-8 · Cited by 304 · see citing works
Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics
Croset, V., Treiber, C. D., & Waddell, S. (2018) · eLife · doi:10.7554/elife.34550 · Cited by 299 · see citing works
The glia of the adult D rosophila nervous system
Kremer, M. C., Jung, C., Batelli, S., Rubin, G. M., & Gaul, U. (2017) · Glia · doi:10.1002/glia.23115 · Cited by 297 · see citing works
Temporal patterning of Drosophila medulla neuroblasts controls neural fates
Li, X., Erclik, T., Bertet, C., Chen, Z., Voutev, R., Venkatesh, S., Morante, J., Çelik, A., & Desplan, C. (2013) · Nature · doi:10.1038/nature12319 · Cited by 288 · see citing works
Candidate Gustatory Interneurons Modulating Feeding Behavior in the Drosophila Brain
Melcher, C., & Pankratz, M. J. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030305 · Cited by 278 · see citing works
Glycolytic enzymes localize to synapses under energy stress to support synaptic function
Jang, S., Nelson, J. C., Bend, E. G., Rodríguez-Laureano, L., Tueros, F. G., Cartagenova, L., Underwood, K., Jorgensen, E. M., & Colón-Ramos, D. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.011 · Cited by 270 · see citing works
Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-Cell RNA Sequencing
Li, H., Horns, F., Wu, B., Xie, Q., Li, J., Li, T., Luginbuhl, D. J., Quake, S. R., & Luo, L. (2017) · Cell · doi:10.1016/j.cell.2017.10.019 · Cited by 256 · see citing works
A Novel MitoTimer Reporter Gene for Mitochondrial Content, Structure, Stress, and Damage in Vivo
Laker, R. C., Xu, P., Ryall, K. A., Sujkowski, A. L., Kenwood, B. M., Chain, K. H., Zhang, M., Royal, M. A., Hoehn, K. L., Driscoll, M., et al. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.530527 · Cited by 254 · see citing works
Using MACS to Identify Peaks from ChIP‐Seq Data
Feng, J., Liu, T., & Zhang, Y. (2011) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0214s34 · Cited by 253 · see citing works
Gradients of the Drosophila Chinmo BTB-Zinc Finger Protein Govern Neuronal Temporal Identity
Zhu, S., Lin, S., Kao, C.-F., Awasaki, T., Chiang, A., & Lee, T. (2006) · Cell · doi:10.1016/j.cell.2006.08.045 · Cited by 249 · see citing works
Klaes, A., Menne, T. V., Stollewerk, A., Scholz, H., & Klämbt, C. (1994) · Cell · doi:10.1016/0092-8674(94)90581-9 · Cited by 238 · see citing works
Bainton, R. J., Tsai, L. T.-Y., Schwabe, T., DeSalvo, M. K., Gaul, U., & Heberlein, U. (2005) · Cell · doi:10.1016/j.cell.2005.07.029 · Cited by 228 · see citing works
Phenotypic convergence: distinct transcription factors regulate common terminal features
Konstantinides, N., Kapuralin, K., Fadil, C., Barboza, L., Satija, R., & Desplan, C. (2018) · Cell · doi:10.1016/j.cell.2018.05.021 · Cited by 226 · see citing works
Self-citationGotō, A., Kadowaki, T., & Kitagawa, Y. (2003) · Developmental Biology · doi:10.1016/j.ydbio.2003.06.001 · Cited by 215 · see citing works
Calcitonin Gene-Related Peptide Neurons Mediate Sleep-Specific Circadian Output in Drosophila
Kunst, M., Hughes, M. E., Raccuglia, D., Felix, M., Li, M., Barnett, G., Duah, J., & Nitabach, M. N. (2014) · Current Biology · doi:10.1016/j.cub.2014.09.077 · Cited by 213 · see citing works
Awasaki, T., Saito, M., Sone, M., Suzuki, E., Sakai, R., Ito, K., & Hama, C. (2000) · Neuron · doi:10.1016/s0896-6273(00)81143-5 · Cited by 206 · see citing works
Luan, H., Lemon, W. C., Peabody, N. C., Pohl, J. B., Zelensky, P. K., Wang, D., Nitabach, M. N., Holmes, T. C., & White, B. H. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.3916-05.2006 · Cited by 205 · see citing works
Mapping Peptidergic Cells in Drosophila: Where DIMM Fits In
Park, D., Veenstra, J. A., Park, J. H., & Taghert, P. H. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001896 · Cited by 204 · see citing works
i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly
Imrichová, H., Hulselmans, G., Kalender Atak, Z., Potier, D., & Aerts, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv395 · Cited by 202 · see citing works
Mapping the Neural Substrates of Behavior
Robie, A. A., Hirokawa, J., Edwards, A. W., Umayam, L. A., Lee, A., Phillips, M. L., Card, G. M., Korff, W. L., Rubin, G. M., Simpson, J. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.032 · Cited by 199 · see citing works
Ig Superfamily Ligand and Receptor Pairs Expressed in Synaptic Partners in Drosophila
Tan, L., Zhang, K. X., Pecot, M. Y., Nagarkar-Jaiswal, S., Lee, P.-T., Takemura, S.-Y., McEwen, J. M., Nern, A., Xu, S., Tadros, W., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.021 · Cited by 190 · see citing works
Novel Features of Cryptochrome-Mediated Photoreception in the Brain Circadian Clock ofDrosophila
Klarsfeld, A., Malpel, S., Michard‐Vanhée, C., et al. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.3661-03.2004 · Cited by 177 · see citing works
Opposing intrinsic temporal gradients guide neural stem cell production of varied neuronal fates
Liu, Z., Yang, C.-P., Sugino, K., Fu, C.-C., Liu, L., Yao, X., Lee, L. P., & Lee, T. (2015) · Science · doi:10.1126/science.aad1886 · Cited by 174 · see citing works
Systematic analysis of neural projections reveals clonal composition of the Drosophila brain
Ito, M., Masuda, N., Shinomiya, K., Endo, K., & Ito, K. (2013) · Current Biology · doi:10.1016/j.cub.2013.03.015 · Cited by 166 · see citing works
atonal Regulates Neurite Arborization but Does Not Act as a Proneural Gene in the Drosophila Brain
Hassan, B. A., Bermingham, N. A., He, Y., Sun, Y., Jan, Y. N., Zoghbi, H. Y., & Bellen, H. J. (2000) · Neuron · doi:10.1016/s0896-6273(00)81059-4 · Cited by 162 · see citing works
Syed, M. H., Mark, B., & Doe, C. Q. (2017) · eLife · doi:10.7554/elife.26287 · Cited by 161 · see citing works
Boll, W., & Noll, M. (2002) · Development · doi:10.1242/dev.00157 · Cited by 156 · see citing works
From Lineage to Wiring Specificity
Komiyama, T., Johnson, W. A., Luo, L., & Jefferis, G. S. X. E. (2003) · Cell · doi:10.1016/s0092-8674(03)00030-8 · Cited by 155 · see citing works
Integration of temporal and spatial patterning generates neural diversity
Erclik, T., Li, X., Courgeon, M., Bertet, C., Chen, Z., Baumert, R., Ng, J., Koo, C., Arain, U., Behnia, R., et al. (2017) · Nature · doi:10.1038/nature20794 · Cited by 153 · see citing works
Multiple neuropeptides in the <i>Drosophila</i> antennal lobe suggest complex modulatory circuits
Carlsson, M. A., Diesner, M., Schachtner, J., & Nässel, D. R. (2010) · Journal of Comparative Neurology · doi:10.1002/cne.22405 · Cited by 146 · see citing works
Abruzzi, K. C., Zadina, A. N., Luo, W., Evelyn, W., Rahman, R., Guo, F., Shafer, O. T., & Rosbash, M. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006613 · Cited by 141 · see citing works
The Drosophila surface glia transcriptome: evolutionary conserved blood-brain barrier processes
DeSalvo, M. K., Hindle, S. J., Rusan, Z. M., Orng, S., Eddison, M., Halliwill, K. D., & Bainton, R. J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00346 · Cited by 139 · see citing works
Analysis of Glial Cell Development and Function in DrosophilaPreprint
Stork, T., Bernardos, R. L., & Freeman, M. R. (2011) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top067587 · Cited by 135 · see citing works
Concentric zones, cell migration and neuronal circuits in the Drosophila visual center
Hasegawa, E., Kitada, Y., Kaido, M., Takayama, R., Awasaki, T., Tabata, T., & Sato, M. (2011) · Development · doi:10.1242/dev.058370 · Cited by 128 · see citing works
Glutamate, GABA and Acetylcholine Signaling Components in the Lamina of the Drosophila Visual System
Kołodziejczyk, A. M., Sun, X., Meinertzhagen, I. A., & Nässel, D. R. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002110 · Cited by 121 · see citing works
Parry, T. J., Theisen, J. W. M., Hsu, J.-Y., Wang, Y., Corcoran, D. L., Eustice, M., Ohler, U., & Kadonaga, J. T. (2010) · Genes & Development · doi:10.1101/gad.1951110 · Cited by 121 · see citing works
Johard, H. A. D., Enell, L. E., Gustafsson, E., Trifilieff, P., Veenstra, J. A., & Nässel, D. R. (2008) · Journal of Comparative Neurology · doi:10.1002/cne.21636 · Cited by 120 · see citing works
Ferree, A. W., Trudeau, K. M., Zik, E., Benador, I. Y., Twig, G., Gottlieb, R. A., & Shirihai, O. S. (2013) · Autophagy · doi:10.4161/auto.26503 · Cited by 119 · see citing works
The great unravelling: chromatin as a modulator of the aging process
O’Sullivan, R. J., & Karlseder, J. (2012) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2012.08.001 · Cited by 115 · see citing works
Karuppudurai, T., Lin, T.-Y., Ting, C.-Y., Pursley, R., Melnattur, K. V., Diao, F., White, B. H., Macpherson, L. J., Gallio, M., Pohida, T., et al. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.010 · Cited by 110 · see citing works
A Drosophila haemocyte-specific protein, hemolectin, similar to human von Willebrand factor
Gotō, A., Kumagai, T., Kumagai, C., et al. (2001) · Biochemical Journal · doi:10.1042/0264-6021:3590099 · Cited by 109 · see citing works
Crocker, A., Guan, X.-J., Murphy, C. T., & Murthy, M. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.04.046 · Cited by 108 · see citing works
Diverse roles for theDrosophilafructose sensor Gr43a
Miyamoto, T., & Amrein, H. (2013) · Fly · doi:10.4161/fly.27241 · Cited by 105 · see citing works
A Drosophila haemocyte-specific protein, hemolectin, similar to human von Willebrand factor
Gotō, A., Kumagai, T., Kumagai, C., et al. (2001) · Biochemical Journal · doi:10.1042/bj3590099 · Cited by 84 · see citing works
Light, heat, action: neural control of fruit fly behaviour
Owald, D., Lin, S., & Waddell, S. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0211 · Cited by 83 · see citing works
Identification of a Peptidergic Pathway Critical to Satiety Responses in Drosophila
Min, S., Chae, H.-S., Jang, Y.-H., Choi, S., Lee, S., Jeong, Y. T., Jones, W. D., Moon, S. J., Kim, Y., & Chung, J. (2016) · Current Biology · doi:10.1016/j.cub.2016.01.029 · Cited by 83 · see citing works
Intrinsic Control of Precise Dendritic Targeting by an Ensemble of Transcription Factors
Komiyama, T., & Luo, L. (2007) · Current Biology · doi:10.1016/j.cub.2006.11.067 · Cited by 73 · see citing works
A Signaling Network for Patterning of Neuronal Connectivity in the Drosophila Brain
Srahna, M., Leyssen, M., Choi, C. M., Fradkin, L. G., Noordermeer, J. N., & Hassan, B. A. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040348 · Cited by 73 · see citing works
Multiple Redundant Medulla Projection Neurons Mediate Color Vision in Drosophila
Melnattur, K. V., Pursley, R. H., Lin, T., Ting, C., Smith, P. D., Pohida, T. J., & Lee, C. (2014) · Journal of Neurogenetics · doi:10.3109/01677063.2014.891590 · Cited by 69 · see citing works
Imp Promotes Axonal Remodeling by Regulating profilin mRNA during Brain Development
Médioni, C., Ramialison, M., Ephrussi, A., & Besse, F. (2014) · Current Biology · doi:10.1016/j.cub.2014.02.038 · Cited by 67 · see citing works
Hamanaka, Y., & Meinertzhagen, I. A. (2009) · The Journal of Comparative Neurology · doi:10.1002/cne.22268 · Cited by 54 · see citing works
Hasegawa, E., Kaido, M., Takayama, R., & Sato, M. (2013) · Developmental Biology · doi:10.1016/j.ydbio.2013.02.012 · Cited by 51 · see citing works
Spindler, S. R., Ortiz, I., Fung, S., et al. (2009) · Developmental Biology · doi:10.1016/j.ydbio.2009.07.035 · Cited by 48 · see citing works
Foster, J. K. (2002) · Brain · doi:10.1093/brain/125.2.439 · Cited by 48 · see citing works
Chen, Z., Rodríguez, A. D. V., Li, X., Erclik, T., Fernandes, V. M., & Desplan, C. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.03.061 · Cited by 42 · see citing works
Porcelli, D., Barsanti, P., Pesole, G., & Caggese, C. (2007) · BMC Evolutionary Biology · doi:10.1186/1471-2148-7-215 · Cited by 38 · see citing works
A Conserved Role for p48 Homologs in Protecting Dopaminergic Neurons from Oxidative Stress
Dib, P. B., Gnägi, B., Daly, F., Sabado, V., Tas, D., Glauser, D. A., Meister, P., & Nagoshi, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004718 · Cited by 37 · see citing works
Single cell transcriptomic profiling of mouse pancreatic progenitors
Stanescu, D. E., Yu, R., Won, K., & Stoffers, D. A. (2016) · Physiological Genomics · doi:10.1152/physiolgenomics.00114.2016 · Cited by 35 · see citing works
robo2androbo3interact witheagleto regulate serotonergic neuron differentiation
Couch, J. A., Chen, J. L., Rieff, H. I., Uri, E. M., & Condron, B. G. (2004) · Development · doi:10.1242/dev.00962 · Cited by 29 · see citing works
Age-Related RNA Decline in Adult Drosophila melanogaster
Tahoe, N. M., Mokhtarzadeh, A., & Curtsinger, J. W. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.9.b896 · Cited by 26 · see citing works
Bernardo-Garcia, F. J., Fritsch, C., & Sprecher, S. G. (2016) · Development · doi:10.1242/dev.128801 · Cited by 25 · see citing works
Hadžić, T., Park, D., Abruzzi, K. C., Yang, L., Trigg, J. S., Rohs, R., Rosbash, M., & Taghert, P. H. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1377 · Cited by 24 · see citing works
RNA-Seq Transcriptome Analysis of Direction-Selective T4/T5 Neurons in Drosophila
Pankova, K., & Borst, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163986 · Cited by 21 · see citing works
Neuropeptide Mapping ofDimmedCells of AdultDrosophilaBrain
Diesner, M., Predel, R., & Neupert, S. (2018) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-017-1870-1 · Cited by 19 · see citing works
Schinaman, J. M., Giesey, R. L., Mizutani, C. M., Lukácsovich, T., & Sousa‐Neves, R. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001964 · Cited by 16 · see citing works
Minocha, S., Boll, W., & Noll, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176002 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Confronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,017 · see citing works
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works
Self-citationSingle-cell transcriptomic profiling of the aging mouse brain
Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0491-3 · Cited by 736 · see citing works
The connectome of an insect brain
Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., Fushiki, A., Andrade, I. V., Khandelwal, A., Valdes-Aleman, J., et al. (2023) · Science · doi:10.1126/science.add9330 · Cited by 435 · see citing works
Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging
Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.008 · Cited by 349 · see citing works
A genetic, genomic, and computational resource for exploring neural circuit function
Davis, F. P., Nern, A., Picard, S., Reiser, M. B., Rubin, G. M., Eddy, S. R., & Henry, G. L. (2020) · eLife · doi:10.7554/elife.50901 · Cited by 268 · see citing works
Neuronal diversity and convergence in a visual system developmental atlas
Özel, M. N., Simon, F., Jafari, S., Holguera, I., Chen, Y.-C., Benhra, N., El-Danaf, R. N., Kapuralin, K., Malin, J. A., Konstantinides, N., et al. (2021) · Nature · doi:10.1038/s41586-020-2879-3 · Cited by 237 · see citing works
Self-citationSingle-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
Eckstein, N., Bates, A. S., Champion, A., Du, M., Yin, Y., Schlegel, P., Lu, A. K.-Y., Rymer, T., Finley-May, S., Paterson, T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.016 · Cited by 233 · see citing works
“…in which gene transcripts or proteins involved in the pathway of interest have been made visible using fluorescent probes (Hyatt and Wise, 2001; Long et al., 2017; Meissner et al., 2019), or by RNA sequencing (Henry et al., 2012; Konstantinides et al., 2015; Davie et al., 2018; Davis et al., 2020).”
Phenotypic convergence: distinct transcription factors regulate common terminal features
Konstantinides, N., Kapuralin, K., Fadil, C., Barboza, L., Satija, R., & Desplan, C. (2018) · Cell · doi:10.1016/j.cell.2018.05.021 · Cited by 226 · see citing works
Self-citationTutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging
Kimmel, J. C., Penland, L., Rubinstein, N. D., Hendrickson, D. G., Kelley, D. R., & Rosenthal, A. Z. (2019) · Genome Research · doi:10.1101/gr.253880.119 · Cited by 184 · see citing works
“Our results are conceptually consistent with previous reports of large transcriptional changes with age in some cell identities and more subtle changes in others (Kowalczyk et al. 2015; Keyes et al. 2016; Davie et al. 2018; Keyes and Fuchs 2018).”
ResultA single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord
Allen, A. M., Neville, M. C., Birtles, S., Croset, V., Treiber, C. D., Waddell, S., & Goodwin, S. F. (2020) · eLife · doi:10.7554/elife.54074 · Cited by 177 · see citing works
Transcriptional Programs of Circuit Assembly in the Drosophila Visual System
Kurmangaliyev, Y. Z., Yoo, J., Valdes-Aleman, J., Sanfilippo, P., & Zipursky, S. L. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.006 · Cited by 166 · see citing works
Bhattacherjee, A., Djekidel, M. N., Chen, R., Chen, W., Tuesta, L. M., & Zhang, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-12054-3 · Cited by 174 · see citing works
Transcriptional and spatial resolution of cell types in the mammalian habenula
Hashikawa, Y., Hashikawa, K., Rossi, M. A., Basiri, M. L., Liu, Y., Johnston, N. L., Ahmad, O. R., & Stuber, G. D. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.011 · Cited by 167 · see citing works
Decoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works
Self-citationDecoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · see citing works
Self-citationNeuronal wiring diagram of an adult brain
Dorkenwald, S., Matsliah, A., Sterling, A. R., Schlegel, P., Yu, S.-C., McKellar, C. E., Lin, A., Costa, M., Eichler, K., Yin, Y., et al. (2023) · doi:10.1101/2023.06.27.546656 · Cited by 149 · see citing works
Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162 · see citing works
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · see citing works
Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level
Lin, Q., Manley, J., Helmreich, M., Schlumm, F., Li, J. M., Robson, D. N., Engert, F., Schier, A., Nöbauer, T., & Vaziri, A. (2020) · Cell · doi:10.1016/j.cell.2019.12.018 · Cited by 122 · see citing works
Eckstein, N., Bates, A. S., Champion, A., Du, M., Yin, Y., Schlegel, P., Lu, A. K.-Y., Rymer, T., Finley-May, S., Paterson, T., et al. (2020) · doi:10.1101/2020.06.12.148775 · Cited by 115 · 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
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778 · see citing works
Self-citationAxonal energy metabolism, and the effects in aging and neurodegenerative diseases
Yang, S., Park, J. H., & Lu, H.-C. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00634-3 · Cited by 94 · see citing works
Sterne, G. R., Otsuna, H., Dickson, B. J., & Scott, K. (2021) · eLife · doi:10.7554/elife.71679 · Cited by 93 · see citing works
A panoramic view of cell population dynamics in mammalian aging
Zhang, Z., Schaefer, C., Jiang, W., Lu, Z., Lee, J., Sziraki, A., Abdulraouf, A., Wick, B., Haeussler, M., Li, Z., et al. (2025) · Science · doi:10.1126/science.adn3949 · Cited by 95 · see citing works
Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics
Bates, A. S., Janssens, J., Jefferis, G. S., & Aerts, S. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.012 · Cited by 70 · see citing works
Self-citationMitochondrial origins of the pressure to sleep
Sarnataro, R., Velasco, C. D., Monaco, N., Kempf, A., & Miesenböck, G. (2025) · Nature · doi:10.1038/s41586-025-09261-y · Cited by 62 · see citing works
Temporal changes in the gene expression heterogeneity during brain development and aging
Işıldak, U., Somel, M., Thornton, J. M., & Dönertaş, H. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-60998-0 · Cited by 53 · see citing works
Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq
Mylka, V., Matetovici, I., Poovathingal, S., Aerts, J., Vandamme, N., Seurinck, R., Verstaen, K., Hulselmans, G., Van den Hoecke, S., Scheyltjens, I., et al. (2022) · Genome Biology · doi:10.1186/s13059-022-02628-8 · Cited by 53 · see citing works
Self-citationUncovering the genetic blueprint of the <i>C. elegans</i> nervous system
Kovács, I. A., Barabási, D. L., & Barabási, A.-L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2009093117 · Cited by 42 · see citing works
Gliotransmission of D-serine promotes thirst-directed behaviors in Drosophila
Park, A., Croset, V., Otto, N., Agarwal, D., Treiber, C. D., Meschi, E., Sims, D., & Waddell, S. (2022) · Current Biology · doi:10.1016/j.cub.2022.07.038 · Cited by 45 · see citing works
Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
Heydari, A. A., & Sindi, S. S. (2023) · Biophysics Reviews · doi:10.1063/5.0091135 · Cited by 44 · see citing works
Peng, G., Cui, G., Ke, J., & Jing, N. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120219-083220 · Cited by 42 · see citing works
Connectomic features underlying diverse synaptic connection strengths and subcellular computation
Liu, T. X., Davoudian, P. A., Lizbinski, K. M., & Jeanne, J. M. (2022) · Current Biology · doi:10.1016/j.cub.2021.11.056 · Cited by 39 · see citing works
TM2D genes regulate Notch signaling and neuronal function in Drosophila
Salazar, J. L., Yang, S.-A., Lin, Y. Q., Li-Kroeger, D., Marcogliese, P. C., Deal, S. L., Neely, G. G., & Yamamoto, S. (2021) · PLOS Genetics · doi:10.1371/journal.pgen.1009962 · Cited by 13 · see citing works
Proteomic aging signatures across mouse organs and life stages
Scifo, E., Morsy, S., Liu, T., Xie, K., Schaaf, K., Bano, D., & Ehninger, D. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00509-x · Cited by 10 · see citing works
The RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain
Higginson, L. A., Wang, X., He, K., Torstrick, M., Kim, M., Wijeratne, H. R. S., Runnebohm, A. M., Benayoun, B. A., MacLean, A., Chanfreau, G. F., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116729 · Cited by 7 · see citing works
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5 · see citing works
Self-citationIdentifying determinants of synaptic specificity by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · doi:10.1101/2023.04.03.534791 · Cited by 39 · see citing works
Acri, D. J., Mustaklem, R., Horan-Portelance, L., Dabin, L. C., Park, J. H., Hartigan, K. A., Kersey, H. N., Mesecar, M. E., Gibbs, J. R., Cookson, M. R., et al. (2026) · bioRxiv · doi:10.64898/2026.07.30.741795
“…reference inputs in this study: all cell types from multiple regions of the mouse brain 20 , human glial substates derived from integrative analysis of 4 multi-region neurodegeneration single nuclei atlases 29 , and PBMCs from individuals infected with influenza, COVID-19, or healthy controls 30 .”
- Authors
- Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., Poovathingal, S., Hulselmans, G., Spanier, K. I., Moerman, T., Vanspauwen, B., Geurs, S., Voet, T., Lammertyn, J., Thienpont, B., Liu, S., Konstantinides, N., Fiers, M., Verstreken, P., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Vlaams Instituut voor Biotechnologie (BE)
- IMEC (BE)
- KU Leuven (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 174
- Issue
- 4
- Pages
- 982-998.e20
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6086935
- OpenAlex
- W2807884764
- Cited by
- 840
- References
- 109
- Citations by year
- 2026: 70 · 2025: 136 · 2024: 112 · 2023: 116 · 2022: 99 · 2021: 113 · 2020: 108 · 2019: 65 · 2018: 21
- FWCI
- 27.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Funders
- The Research Foundation - Flanders (G.0640.13, G.0791.14)
- Special Research Fund (BOF) KU Leuven (PF/10/016, OT/13/103, Opening the Future” grant, Methusalem Grant (3M140280))
- Flemish Government (ERC consolidator, 646671 RobustSynapses, 724226 cis-CONTROL, PDM/16/188)
- agency for Innovation by Science and Technology (141509, 1199518N)
- Deutsche Forschungsgemeinschaft (808066)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
- Keywords
- brain aging, Drosophila melanogaster, single-cell transcriptome atlas, neuronal cell types, cellular heterogeneity, gene regulatory networks, energy metabolism
- MeSH terms
- Aging, Animals, Brain, Drosophila melanogaster, Female, Male, Gene Expression Profiling, Drosophila Proteins, Gene Regulatory Networks, Single-Cell Analysis, 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
