The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Single-cell genomics identifies cell type–specific molecular changes in autism
Open accessAbstract
Brain cell transcriptomes in autism: Autism manifests in many ways. Despite that diversity, the disorder seems to affect specific cellular pathways, including those observed in the neocortex of patients' brains. Velmeshev et al. analyzed the transcriptomes of single brain cells, including neurons and glia, from patients with autism. Single-nucleus RNA sequencing analysis suggested that affected pathways regulate synapse function as well as neural outgrowth and migration. Furthermore, in patient samples, specific sets of genes enriched in upper-layer projection neurons and microglia correlated with clinical severity. Science , this issue p. 685
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“Of note, one study used single cell technologies to examine specific cell types and developmental stages using brain tissue from people with ASD 60 , and demonstrated changes in transcription in multiple cell types including upper-layer cortical neurons.”
From Autism spectrum disorder (2020) · cites this paper as [60]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Of the 121,451 cells in this dataset, 20,249 were labeled as inhibitory neurons based on marker gene expression profiles.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as Velmeshev et al.9
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“Cell type-specific alterations have been observed in patients with ASD and brain organoid disease models11,34.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [34]
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“CHCHD2 encodes a mitochondrial protein, and its downregulation has been observed in neurons from the postmortem brains of patients with ASD34.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“This aligns with the observation that ASD risk gene coexpression networks are enriched in upper-layer neurons during development and that these neurons are preferentially affected in ASD patients6,34.”
From Single-cell brain organoid screening identifies developmental defects in autism (2023) · cites this paper as [34]
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“Network analysis of inhibitory neurons from published single-nucleus RNA sequencing (snRNA-seq) in autism spectrum disorder (ASD) donors9 revealed modules disrupted in ASD containing key genetic risk genes such as SCN2A, TSC1, and SHANK2.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [9]
In the citing paper
“From this analysis, we identified two genes, SST in interneurons and NRN1 in excitatory neurons, both of which showed decreased expression in ASD patients and were likewise significantly decreased in expression across our panel of perturbations (FDR < 0.1), albeit with different effect sizes (Fig. 4F, Table S10).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [31]
In the citing paper
“From this analysis, we identified two genes, SST in interneurons and NRN1 in excitatory neurons, both of which showed decreased expression in ASD patients and were likewise significantly decreased in expression across our panel of perturbations (FDR < 0.1), albeit with different effect sizes (Fig. 4F, Table S10).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [31]
In the citing paper
“We identified genes that were differentially expressed between patients and controls in at least one of three major cell types (inhibitory neurons, excitatory neurons, or oligodendrocytes) with FDR < 0.2, and selected those that have 1:1 orthologs in mice, resulting in 14 genes (Fig. 4F, Table S10).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [31]
In the citing paper
“Using a dataset of snRNA-seq profiles from 15 ASD donors and 16 controls (31), we defined differentially expressed (DE) genes in each cell type using a statistically conservative pseudobulk-based analysis with DESeq2 (35, 36), correcting for age, sex, and patient-to-patient variability.”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [31]
In the citing paper
“Such a pattern of dysfunction has previously been reported in an snRNA-seq study of autism and correlated with cognitive deficits8.”
From Dysregulation of brain and choroid plexus cell types in severe COVID-19 (2021) · cites this paper as [8]
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Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
(2008) · Journal of Machine Learning Research · Cited by 33,051
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
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
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Comprehensive integration of single-cell dataPreprint
Stuart, T., Butler, A. W., Hoffman, P. J., Hafemeister, C., Papalexi, E., Mauck, W., Hao, Y.-H., Stoeckius, M., Smibert, P., & Satija, R. (2018) · bioRxiv · doi:10.1101/460147 · Cited by 13,858
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,457
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
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Research works printed but not linked: 4
- F. K. Satterstrom. Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk. bioRxiv 484113 [preprint]. 1 December 2018.
- 10.1093/bioinformatics/btt656 · DOI · Paper page
- 10.1093/bioinformatics/btt656 · DOI · Paper page
- 10.1093/bioinformatics/btt656 · DOI · Paper page
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Items 801–900 of 920
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Self-citationWoodard, G. E., & Julien, C. (2026) · Behavioural Brain Research · doi:10.1016/j.bbr.2026.116267
Zhang, W., Wang, C., Zhao, X. X., Pang, S., Zhao, Z., Zhang, G., & Chen, J. (1970) · Current Gene Therapy · doi:10.2174/0115665232499854260512155018
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Li, L., Saw, T. Y., Chou, N., Goh, J. L. J., Kwa, J. E., Chock, W. K., Singhal, V., Li, Z., Huang, M. J., Ng, H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74320-5
Akela, M. A., Qamar, M. T. U., & Alamri, M. A. (2026) · Molecular Diversity · doi:10.1007/s11030-026-11612-4
Lian, A., Wang, Q., He, M., & Zhang, H. (2026) · Functional & Integrative Genomics · doi:10.1007/s10142-026-01971-4
Kurmashev, R. (2026) · International Journal of Developmental Neuroscience · doi:10.1002/jdn.70157
Human Cellular Models of Autism and Related Conditions
Guttikonda, S. R., & Makinson, C. D. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.07.003
Single-nucleus transcriptomics-based drug screening platform for focal cortical dysplasia
Fang, C., Meng, G., Yang, L., Wang, L., Tan, Y., Guo, J., Shi, Y., Liu, X., Zhao, R., Zhang, M., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03760-8
Lai, X., & Wen, J. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111835
Kurmashev, R. (2026) · American Journal of Pure and Applied Biosciences · doi:10.34104/ajpab.026.05210528
Transcriptomic Profiling of the Amygdala of Children With Autism Spectrum Disorder
Babu, J., Lal, A., Challagundla, L., Allen, O., Griffin, M. L., Gisabella, B., & Pantazopoulos, H. (2026) · Autism Research · doi:10.1002/aur.70333
Pan, J., Zhang, H., Zhai, Y., Zhang, J., & Deng, H. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1780430
Herdt, D. D., Lefevere, E., Brouwers, V., Hartvig, L., Geeraerts, E., Hsiao, C., Chen, J. Q. A., Pinto, R., Duvey, G., Burbidge, S., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354525
Exploring ferroptosis: Unraveling its potential role in autistic spectrum disorder
Abaszadeh, Y., Khalifeh, S., Sala, C., & Moghaddam, P. M. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106893
Decoding Brain Development and Function Through GABAergic Inhibitory Neurons
Batista‐Brito, R., Mayer, C., Paredes, M., & Silva, C. G. (2026) · European Journal of Neuroscience · doi:10.1111/ejn.70650
Wu, X., Yang, L., Fan, Y., Li, X., Shi, X., Dai, C., Pang, Y., Wang, J., Cun, Y., & Dong, Z. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03831-w
Saxena, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10789747/v1
Metadata
- Authors
- Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R.
- Affiliations
- University of California, San Francisco (US)
- University of California, Santa Cruz (US)
- Corresponding author
- Velmeshev, Dmitry
- Kriegstein, Arnold R.
- ORCID
- Venue
- Science
- Published
- 2019-05-17
- Volume
- 364
- Issue
- 6441
- Pages
- 685-689
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7678724
- OpenAlex
- W2946016273
- Cited by
- 927
- References
- 27
- Citations by year
- 2026: 110 · 2025: 131 · 2024: 150 · 2023: 147 · 2022: 138 · 2021: 117 · 2020: 113 · 2019: 18
- FWCI
- 56.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- National Institute of Mental Health (1U01MH114825-01)
- National Human Genome Research Institute (4U41HG002371)
- Simons Foundation (515488)
- California Institute for Regenerative Medicine (GC1R-06673-C)
- Silicon Valley Community Foundation (2018-182809)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- autism spectrum disorder, single-nucleus RNA sequencing, neocortex, excitatory neurons, microglia, synaptic signaling, clinical severity, cell type-specific transcriptomics, cortical circuits
- MeSH terms
- Adolescent, Autistic Disorder, Cell Nucleus, Child, Child, Preschool, Female, Gene Expression Regulation, Humans, Male, Neurons, Sequence Analysis, RNA, Microglia, Neocortex, Gene Expression Profiling, Genomics, Young Adult, Single-Cell Analysis
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