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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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In the citing paper
“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]
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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“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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Citations
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
Cited by
Sorted by citations
Items 701–800 of 920
Acetaminophen produces limited molecular and functional effects in human cortical organoids
Trovò, L., Kucera, G., Gómez, G., de Souza, J. S., Muotri, A. R., & Iakoucheva, L. M. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03920-w · Cited by 1
Hao, Y., Park, C. Y., Theesfeld, C. L., & Troyanskaya, O. G. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02470-2 · Cited by 1
[Advances on GABAergic interneurons in autism spectrum disorders].
Li, J., Xu, J., & Luo, J. (2020) · PubMed Central · doi:10.3785/j.issn.1008-9292.2020.08.10 · Cited by 1
Functional Connectivity in Autism Spectrum Disorders: Challenges and Perspectives
Müller, R., & Linke, A. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_12 · Cited by 1
Portrait of a research field: astrocytes in autism
Schenkman, L. (2022) · The Transmitter · doi:10.53053/zjbk3878 · Cited by 1
The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency
Smith, N. R., Ameen, S., Miller, S. N., Kasper, J. M., Schwarz, J. M., Hommel, J. D., & Borzou, A. (2024) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2024.1417346 · Cited by 1
Expanding canonical cortical cell type markers in the era of single-cell transcriptomics
Joshy, D. M., & Yi, S. V. (2026) · Scientific Reports · doi:10.1038/s41598-026-51501-2 · Cited by 1
Assir, M. Z. K., Yanakiev, M., Gim, D. H., Valkila, S. S. M., Muscolino, P., Liu, P., Lecante, L. L., Fowler, P., Berg, D. A., & Kang, E. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02400-2 · Cited by 2
Liu, Y., Shi, B., Wu, A., Peng, X., Yuan, Z., Liu, L., & Liu, L. (2021) · Life · doi:10.3390/life11070716 · Cited by 1
Single cell atlas of trisomy 21 cerebral cortexPreprint
Zhang, J., Wang, S., Xu, Z., Ding, X., Li, C., Zhang, Y., Chen, Y., Zhong, J., Liang, L., Chai, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.01.892398 · Cited by 1
Torshizi, A. D., Duan, J., & Wang, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.28.121483 · Cited by 1
GABAergic signaling promotes early-life seizures in epileptic SYNGAP1 +/- micePreprint
Sullivan, B. J., Kipnis, P. A., Ammanuel, S. G., & Kadam, S. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.14.381749 · Cited by 1
β-catenin signaling via astrocyte-encoded TCF7L2 regulates neuronal excitability and social behaviorPreprint
Szewczyk, Ł. M., Lipiec, M. A., Liszewska, E., Urban‐Ciećko, J., Kondrakiewicz, L., Puścian, A., Knapska, E., Hiromi, I., Jan, N. T., Molofsky, A. V., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.28.402099 · Cited by 1
Bohlen, J. F., Cleary, C. M., Das, D., Sripathy, S. R., Sadowski, N., Shim, G., Kenney, R. F., Buchler, I. P., Mulkey, D. K., & Maher, B. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.03.490512 · Cited by 1
Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applicationsPreprint
Maitra, M., Nagy, C., Wang, Y. C., Nascimento, C. F., Suderman, M. J., Théroux, J., Mechawar, N., Ragoussis, J., & Turecki, G. (2020) · doi:10.21203/rs.3.pex-905/v2 · Cited by 1
Editorial: Brain extracellular matrix: Involvement in adult neural functions and disease volume II
Pantazopoulos, H., & Berretta, S. (2022) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.1009456 · Cited by 1
Yang, K., Ishizuka, K., Lane, A. P., Narita, Z. C., Hayashida, A., Lema, Y. Y., Heffron, E., Loudd, H., Schumacher, M., Kano, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.23.509290 · Cited by 1
Genetics of cortical development
Bella, D. J. D., & Habibi, E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818872-9.00108-4 · Cited by 1
Borcuk, C., Bharadwaj, R. A., Kikidis, G. C., Mallepalli, V., Sportelli, L., Bertolino, A., Cole, R. N., Devine, L. R., Maher, B. J., Sripathy, S. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598714 · Cited by 1
Kendrick, R. M., Linderman, S. W., & Owen, S. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.26.609746 · Cited by 1
Francavilla, R., Chattopadhyaya, B., Kamda, J. L. D., Jadhav, V., Kourrich, S., Michaud, J. L., & Cristo, G. D. (2024) · eLife · doi:10.7554/elife.97100.2 · Cited by 1
Nie, P., Zhan, Y., Chen, R., Qi, R., Liu, C., & Wang, G.-Z. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.11.627878 · Cited by 1
Wang, L., Yang, E., Stolovitzky, G. A., Schadt, E. E., & Zhu, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.17.633619 · Cited by 1
Kvastad, L., Kollotzek, A., Saghaleyni, R., Chang, C. J., Cao, T., & Lappalainen, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.02.651876 · Cited by 1
Song, Y., Tang, M., Liu, Q., Wang, H., Li, Q., Zou, F., & Hou, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.27.690951 · Cited by 1
Zhou, J., Liu, C., Liu, X., Zhang, Y., Wei, Y., Shin, J. H., Maher, B. J., Liu, C., Luo, C., Wang, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.09.710649 · Cited by 1
A multimodal interrogation of Broca’s area in the pediatric and adult human brainPreprint
Moriano, J., Mukhtar, T., Lee, J. T. H., Memi, F., Augustin, J. J., Leonard, R., Asfouri, J., Lu, I.-L., Siebert, C., Choi, J. J.-Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.22.739934 · Cited by 1
Self-citationFranklin, A., Davies, J. P., Chioza, B. A., Burrage, J., Walker, E., Bamford, R. A., Babtie, A. C., Blake, G. E. T., Baron‐Cohen, S., Dempster, E. L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.16.749446 · Cited by 1
Yoo, Y.-E., Zhang, Z., Mandal, P., Tang, Z., Zhang, J., Chen, X., Robinson, M., Eaton, M., Deming, B., Halurkar, M., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04348-0
Novel insights into autism from single-cell genomics
Wood, H. B. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0219-8
Chen, Y., Chen, C.-Y., Mai, T., Chuang, C.-F., Gupta, S. K., Yen, L., Wang, Y.-D., & Chuang, T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/712331
Convergence in neuropsychiatric research
Marx, V. (2019) · Nature Methods · doi:10.1038/s41592-019-0578-5
Bertelsen, N., Landi, I., Bethlehem, R. A. I., Seidlitz, J., Busuoli, E. M., Mandelli, V., Satta, E., Trakoshis, S., Bonnie Auyeung, B., Kundu, P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.08.083758
Gene Screening for Autism Based on Cell-type-specific Predictive Models
Wang, Y., Lin, Y., Ji, G., & Guan, J. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313581
Pinto, B. G. G. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.95.2021.tde-18052021-171202
He, J. (2021) · OpenCommons at University of Connecticut (University of Connecticut)
Samudyata, S., Oliveira, A. O., Malwade, S., de Sousa, N. R., Goparaju, S. K., Orhan, F., Steponaviciute, L., Sheridan, S. D., Perlis, R. H., Rothfuchs, A. G., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-724318/v1
Single cell RNA sequencing in psychiatric disorders
Ruzicka, W. B. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823577-5.00018-0
[Facioacromelic disostosis with metal retardation (author's transl)].
Cantú, J. M., Hernández, A., Nazará-Cazorla, Z., Pacheco, C., & Armendares, S. (1974) · PubMed
Ji, Y., Chen, R., Wang, Q., Wei, Q., Tao, R., & Li, B. (2022) · Genes · doi:10.3390/genes13020381
Extraction of nuclei from archived post-mortem tissues for single-nucleus sequencing applicationsPreprint
Maitra, M., Nagy, C., Wang, Y. C., Nascimento, C. F., Suderman, M. J., Théroux, J., Mechawar, N., Ragoussis, J., & Turecki, G. (2020) · doi:10.21203/rs.3.pex-905/v1
Borner, T., Geisler, C. E., Fortin, S. M., Cosgrove, R., Alsina‐Fernandez, J., Dogra, M., Doebley, S. A., Sanchez-Navarro, M. J., Leon, R. M., Gaisinsky, J., et al. (2021) · doi:10.2337/figshare.15125112
Silver, D. L. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737253619.793571137
Tiwari, V. K., & McComb, J. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737253619.793571682
Cell-associated Transcriptional Alterations in the Retinal of Alzheimer’s DiseasePreprint
Ngolab, J., Mark, A., Buchanan, J., Korouri, S., Priessl, S., Rosenthal, S. B., Wang, A., Fisch, K. M., & Rissman, R. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.18.502974
Bieneck, V. (2022) · Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · doi:10.21248/gups.70645
Consistent, convergent pathways link two forms of autism
Askham, A. V. (2022) · The Transmitter · doi:10.53053/owuw9177
Progress toward defining the molecular identities of neocortical neurons
Deleanu, R. (2021) · Journal of Cell Identity · doi:10.47570/joci.2022.006
Spatial Technologies: A Game Changer for Studying the Tumor Microenvironment
Lee, R. Y., Lau, M. C., Goh, D. L. M., Wee, F. Y. T., Lim, J. C. T., & YEONG, J. P. S. (2023) · Springer eBooks · doi:10.1007/978-3-030-80962-1_96-1 · Cited by 1
Molecular Methods in Neuroscience and Psychiatry
Ivanovska, M., Kalfova, T., Petrov, S., Bozhkova, M., Baldzhieva, A., Taskov, H. B., Stoyanov, D., & Murdjeva, M. (2023) · Neuromethods · doi:10.1007/978-1-0716-3230-7_2
Spatial Technologies: A Game Changer for Studying the Tumor Microenvironment
Lee, R. Y., Lau, M. C., Goh, D. L. M., Wee, F. Y. T., Lim, J. C. T., & YEONG, J. P. S. (2023) · Springer eBooks · doi:10.1007/978-3-030-80962-1_96-2
Wagstyl, K., Adler, S., Jakob, S., et al. (2023) · eLife · doi:10.7554/elife.86933.1.sa1
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
Wang, Y., Ma, S., Ma, R., Xiao, L., Tang, S., Wei, Y., & Pan, Y. F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.26.546426
(2023) · The MIT Press eBooks · doi:10.7551/mitpress/15380.003.0024
Wehbe, F., Adams, L., Yuen, S., Kim, Y., & Tanaka, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.01.569595
The Pathogenesis and Differences in molecule of Autism
Xie, J. (2024) · Theoretical and Natural Science · doi:10.54254/2753-8818/29/20240705
Dysfunctional Circuit Mechanisms of Sensory Processing in FXS and ASD: Insights from Mouse Models
Goel, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-42383-3_1
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621291
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621279
Zhang, M., Liu, S., Miao, Z., Han, F., Gottardo, R., & Sun, W. (2022) · Figshare · doi:10.6084/m9.figshare.19029492
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621276
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621282
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621285
Subramanian, A., Alperovich, M., Yang, Y., & Li, B. (2023) · Figshare · doi:10.6084/m9.figshare.22610146
Zhang, J., Ji, G., Gao, X., & Guan, J. (2023) · Figshare · doi:10.6084/m9.figshare.22621288
Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling ApproachPreprint
Qiao, M. (2024) · eLife · doi:10.7554/elife.91532.2
Opportunities of complex analysis in single-cell RNA sequencing
Khozyainova, A. A., Valyaeva, A. A., Arbatsky, M. S., Isaev, S. V., Iamshchikov, P. S., Volchkov, E., Sabirov, M. S., Zainullina, V. R., & Chechekhin, V. I. (2023) · Biochemistry (Moscow) · doi:10.31857/s032097252302001x
SEQUENCING OF SINGLE CELLS: APPLICATION AND PERSPECTIVES
Дмитриевич, Н. А., Tukhbatullin, A. A., Khannanova, G. R., Альбертовна, Р. Г., & Enikeeva, K. (2024) · Molekulyarnaya Meditsina (Molecular medicine) · doi:10.29296/24999490-2024-02-02
Xu, X. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202419002020
Banerjee, P., Sharma, S. D., Burr, K., Morris, K. J., Ritakari, T. E., Baxter, P. S., Cooper, J. D., Cardinalli, A., Subash, S., Paza, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.24.600387
Coradin, J., de Castro, Y. V. C., Caiado, F. L., Destêfani, A. C., & Destefani, V. C. (2024) · Revista Ibero-Americana de Humanidades, Ciências e Educação · doi:10.51891/rease.v10i8.15296
Epigenetics of Neurodevelopmental Disorders
Campbell, D. B. (2024) · Contemporary clinical neuroscience · doi:10.1007/978-3-031-66932-3_5
Andrade, A. X., Nguyen, S. N., Marckx, A., & Montillo, A. A. (2026) · Nature Communications · doi:10.1038/s41467-026-72666-4
Myers, S. M., & Challman, T. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-824060-1.00038-9
Bi, X., Kang, L., & Wu, X. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821945
New human brain atlas charts gene activity and chromosome accessibility, from embryo to adolescence
Sidik, S. M. (2025) · The Transmitter · doi:10.53053/scpg4853
Fu, W., Pan, J., Yang, X., Zhang, Y., Mo, J., Tang, X., & Liu, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5170674
Is a Synaptic Dysfunction the Origin of Autism?
Bittmann, S. (2025) · Journal of Pediatrics Perinatology and Child Health · doi:10.26502/jppch.74050212
Zhang, L., Yang, L., Ren, Y., Zhang, S., Guan, W., & Chen, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf327
A Brief Introduction to Genomics and Single-cell Analysis for Psychiatric Disorders
NISHIOKA, M. (2025) · Juntendō Igaku/Juntendo igaku · doi:10.14789/ejmj.jmj25-0003-r
Murali, A., Tipgomut, C., Jeyasekharan, A. D. D., & Sinha, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.23.661225
Francavilla, R., Chattopadhyaya, B., Kamda, J. L. D., Jadhav, V., Kourrich, S., Michaud, J. L., & Cristo, G. D. (2025) · eLife · doi:10.7554/elife.97100.3
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Wang, H., Zhu, X., Zhang, H., & Chen, W. (2025) · Genes · doi:10.3390/genes16091109
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Lee, R. Y., Lau, M. C., Goh, D. L. M., Wee, F. Y. T., Lim, J. C. T., & YEONG, J. P. S. (2025) · Springer eBooks · doi:10.1007/978-3-032-00761-2_96
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Xu, Y., Zhang, W., & Wu, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf556
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Zhang, Z., Xu, J., Matthews, E. A., Deasy, S., He, L., Arroyo, D., Pan, E., Deshmukh, A., Kaur, K. S., McNamara, J. O., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.03.686433
Self-citationZhang, Z., D’Arcy, B. R., He, L., Arroyo, D., Deasy, S., Matthews, E. A., Yan, Z., Rastogi, N., Leon, W. M., Kaur, K. S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.07.687321
Self-citation
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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