Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Open accessPublished version of a preprint
Abstract
We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD). This study defined spatial gene expression in the human dorsolateral prefrontal cortex. It reveals layer-enriched expression of genes associated with schizophrenia and autism, highlighting the clinical relevance of spatially defined expression.
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?
Layer marker distributions within AARs agreed with known biology of cortical layers.
“we found that layer marker distributions within our AARs agreed with the biology described in the literature,11,48”
From Uncovering the signatures of aging and senescence in the human dorsolateral prefrontal cortex (2026) · cites this paper as [11]
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
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
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Wickham, H. (2016) · Use R! · doi:10.1007/978-3-319-24277-4 · Cited by 33,612 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · cited 2× · see citing works
“In the ‘unsupervised’ approach to define sub-groups of spots with similar expression profiles in a completely data-driven manner, we considered the possible combinations of (i) two types of methods for dimensionality reduction (top 50 principal components (PCs) with the BiocSingular Bioconductor package (Lun, 2019)…”
Methodvoom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
An anatomically comprehensive atlas of the adult human brain transcriptome
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., van de Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., et al. (2012) · Nature · doi:10.1038/nature11405 · Cited by 3,083 · see citing works
eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568 · see citing works
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424 · see citing works
Identification of common genetic risk variants for autism spectrum disorder
Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, Grove, J., BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0344-8 · Cited by 2,421 · see citing works
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321 · cited 8× · see citing works
“ (Satterstrom et al., 2020), which identified 102 genes with ASD-associated variants.”
BackgroundResultInfluentialMcCarthy, 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
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · cited 11× · see citing works
“Heatmap of Pearson correlation values evaluating the relationship between our derived layer-enriched statistics (y-axis) for 700 genes and (B) layer-enriched statistics from snRNA-seq data in human medial temporal cortex produced by Hodge et al. (Hodge et al., 2019) (these data only profiled layers 1-6 in the gray mat…”
BackgroundMethodInfluentialSynaptic Energy Use and Supply
Harris, J. J., Jolivet, R., & Attwell, D. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.019 · Cited by 1,747 · see citing works
Neuronal subtype specification in the cerebral cortex
Molyneaux, B. J., Arlotta, P., Menezes, J. R. L., & Macklis, J. D. (2007) · Nature reviews. Neuroscience · doi:10.1038/nrn2151 · Cited by 1,632 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552 · see citing works
Genome-wide association study identifies 30 loci associated with bipolar disorder
eQTLGen Consortium, Stahl, E. A., BIOS Consortium, the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0397-8 · Cited by 1,519 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 1,511 · see citing works
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401 · see citing works
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
Self-citationThe neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215 · see citing works
High-definition spatial transcriptomics for in situ tissue profiling
Vickovic, S., Eraslan, G., Salmén, F., Klughammer, J., Stenbeck, L., Schapiro, D., Äijö, T., Bonneau, R., Bergenstråhle, L., Navarro, J. F., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0548-y · Cited by 1,150 · see citing works
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045 · see citing works
Lun, A. T. L., Riesenfeld, S. J., Andrews, T. S., Dao, T. P., Gomes, T., Jamboree, P. I. T. 1. H. C. A., & Marioni, J. C. (2019) · Genome biology · doi:10.1186/s13059-019-1662-y · Cited by 1,044 · 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
DeFelipe, J., & Fariñas, I. (1992) · Progress in Neurobiology · doi:10.1016/0301-0082(92)90015-7 · Cited by 975 · see citing works
Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex
Nowakowski, T. J., Bhaduri, A., Pollen, A. A., Alvarado, B., Mostajo-Radji, M. A., Di Lullo, E., Haeussler, M., Sandoval-Espinosa, C., Liu, S. J., Velmeshev, D., et al. (2017) · Science · doi:10.1126/science.aap8809 · Cited by 964 · see citing works
Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., et al. (2016) · Science · doi:10.1126/science.aaf1204 · Cited by 957 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Self-citationSingle-cell genomics identifies cell type–specific molecular changes in autism
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., & Kriegstein, A. R. (2019) · Science · doi:10.1126/science.aav8130 · Cited by 927 · see citing works
SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs)
Abrahams, B. S., Arking, D. E., Campbell, D. B., Mefford, H. C., Morrow, E. M., Weiss, L. A., Menashe, I., Wadkins, T., Banerjee-Basu, S., & Packer, A. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-36 · Cited by 873 · cited 6× · see citing works
“(A) Autism spectrum disorder (ASD) laminar enrichments for SFARI (Abrahams et al., 2013) and Satterstrom et al (Satterstrom et al.”
BackgroundMethodInfluentialA Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 740 · see citing works
SpatialDE: identification of spatially variable genes
Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695 · see citing works
Genetic identification of brain cell types underlying schizophrenia
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Skene, N. G., Bryois, J., Bakken, T. E., Breen, G., Crowley, J. J., Gaspar, H. A., Giusti-Rodriguez, P., Hodge, R. D., Miller, J. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0129-5 · Cited by 651 · see citing works
Spatial organization of the somatosensory cortex revealed by osmFISH
Codeluppi, S., Borm, L. E., Zeisel, A., Manno, G. L., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018) · Nature Methods · doi:10.1038/s41592-018-0175-z · Cited by 629 · see citing works
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Rajkowska, G., & Goldman, P. S. (1995) · Cerebral Cortex · doi:10.1093/cercor/5.4.323 · Cited by 562 · see citing works
Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
Berglund, E., Maaskola, J., Schultz, N., Friedrich, S., Marklund, M., Bergenstråhle, J., Tarish, F., Tanoglidi, A., Vicković, S., Larsson, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04724-5 · Cited by 550 · see citing works
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
Local protein synthesis is a ubiquitous feature of neuronal pre- and postsynaptic compartments
Hafner, A.-S., Donlin-Asp, P. G., Leitch, B., Herzog, E., & Schuman, E. M. (2019) · Science · doi:10.1126/science.aau3644 · Cited by 431 · see citing works
Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al. (2019) · Science · doi:10.1126/science.aav9776 · Cited by 429 · see citing works
Zeng, H., Shen, E. H., Hohmann, J. G., Oh, S. W., Bernard, A., Royall, J. J., Glattfelder, K. J., Sunkin, S. M., Morris, J. A., Guillozet-Bongaarts, A. L., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.052 · Cited by 426 · see citing works
Self-citationNuclear RNA-seq of single neurons reveals molecular signatures of activation
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S. L., Paquola, A. C. M., Venepally, P., Novotny, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11022 · Cited by 405 · see citing works
Rajkowska, G., & Goldman, P. S. (1995) · Cerebral Cortex · doi:10.1093/cercor/5.4.307 · Cited by 392 · see citing works
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383 · see citing works
The BrainSeq Consortium, Jaffe, A. E., Straub, R. E., Shin, J. H., Tao, R., Gao, Y., Collado-Torres, L., Kam-Thong, T., Xi, H. S., Quan, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0197-y · Cited by 364 · see citing works
Self-citationDetection and removal of barcode swapping in single-cell RNA-seq data
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L., & Marioni, J. C. (2018) · Nature Communications · doi:10.1038/s41467-018-05083-x · Cited by 324 · see citing works
Spatial transcriptomics coming of age
Burgess, D. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0129-z · Cited by 323 · see citing works
Critical Factors in Gene Expression in Postmortem Human Brain: Focus on Studies in Schizophrenia
Lipska, B. K., Deep‐Soboslay, A., Weickert, C. S., Hyde, T. M., Martin, C. E., Herman, M. M., & Kleinman, J. E. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.06.019 · Cited by 280 · see citing works
Self-citationOlmos-Serrano, J. L., Kang, H. J., Tyler, W. A., Silbereis, J. C., Cheng, F., Zhu, Y., Pletikos, M., Jankovic-Rapan, L., Cramer, N. P., Galdzicki, Z., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.042 · Cited by 239 · cited 1× · see citing works
“6) Down syndrome (Olmos-Serrano et al., 2016): 2 gene sets based on DE analyses of patients with Down syndrome versus controls (at FDR < 0.”
BackgroundCollado‐Torres, L., Burke, E. E., Peterson, A., Shin, J. H., Straub, R. E., Rajpurohit, A., Semick, S. A., Ulrich, W. S., Price, A. J., Valencia, C., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.013 · Cited by 231 · see citing works
Self-citationOrganelle motility and metabolism in axons vs dendrites of cultured hippocampal neurons
Overly, C. C., Rieff, H. I., & Hollenbeck, P. J. (1996) · Journal of Cell Science · doi:10.1242/jcs.109.5.971 · Cited by 200 · see citing works
Dendritic spine alterations in schizophrenia
Moyer, C. E., Shelton, M. A., & Sweet, R. A. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.11.042 · Cited by 184 · see citing works
Layer- and Cell Type-Specific Modulation of Excitatory Neuronal Activity in the Neocortex
Radnikow, G., & Feldmeyer, D. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00001 · Cited by 180 · see citing works
Hu, P., Fabyanic, E., Kwon, D. Y., Tang, S., Zhou, Z., & Wu, H. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.11.017 · Cited by 162 · see citing works
Web Application Framework for R [R package shiny version 1.5.0]
Chang, W., Cheng, J. Z., Allaire, J., et al. (2020) · Cited by 162 · see citing works
Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159 · see citing works
Rizzardi, L. F., Hickey, P. F., Rodriguez DiBlasi, V., Tryggvadóttir, R., Callahan, C. M., Idrizi, A., Hansen, K. D., & Feinberg, A. P. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0297-8 · Cited by 146 · see citing works
Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques
He, Z., Han, D., Efimova, O., Guijarro, P., Yu, Q., Oleksiak, A., Jiang, S., Anokhin, K. V., Velichkovsky, B. M., Grünewald, S., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4548 · Cited by 136 · see citing works
Local translation in neuronal processes
Biever, A., Donlin-Asp, P., & Schuman, E. M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.008 · Cited by 132 · see citing works
Cell Type-Specific Structural Organization of the Six Layers in Rat Barrel Cortex
Narayanan, R. T., Udvary, D., & Oberlaender, M. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2017.00091 · Cited by 100 · see citing works
Prenatal Expression Patterns of Genes Associated With Neuropsychiatric Disorders
Birnbaum, R., Jaffe, A. E., Hyde, T. M., Kleinman, J. E., & Weinberger, D. R. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.13111452 · Cited by 97 · see citing works
Self-citationJaffe, A. E., Hoeppner, D. J., Saito, T., Blanpain, L. T., Ukaigwe, J., Burke, E. E., Collado‐Torres, L., Tao, R., Tajinda, K., Maynard, K. R., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0604-z · Cited by 95 · see citing works
Self-citationMaynard, K. R., Tippani, M., Takahashi, Y., Phan, B. N., Hyde, T. M., Jaffe, A. E., & Martinowich, K. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa312 · Cited by 83 · see citing works
Self-citationGregory, J. M., McDade, K., Livesey, M. R., Croy, I., de Procé, S. M., Aitman, T. J., Chandran, S., & Smith, C. (2020) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12597 · Cited by 41 · see citing works
Crowell, H. L., Soneson, C., Germain, P.-L., et al. (2019) · bioRxiv · doi:10.1101/713412 · Cited by 40 · see citing works
Moncada, R., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/254375 · Cited by 38 · see citing works
Mapping synaptic pathology within cerebral cortical circuits in subjects with schizophrenia
Sweet, R. A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00044 · Cited by 33 · see citing works
SA75LARGE-SCALE TRANSCRIPTOME-WIDE CHARACTERIZATION OF ASD, SCHIZOPHRENIA, AND BIPOLAR DISORDER
Gandal, M. J., Zhang, P., Walker, R. L., Won, H., Hadjimichael, E., van Bakel, H., Haney, J. R., Jaffe, A. E., White, K. P., Peters, M. A., et al. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.08.297 · Cited by 33 · see citing works
Self-citationMartin Morgan, V. O. (2017) · NCI BioConductor · doi:10.18129/b9.bioc.summarizedexperiment · Cited by 27 · see citing works
Aaron Lun [Aut, C. (2017) · NCI BioConductor · doi:10.18129/b9.bioc.singlecellexperiment · Cited by 14 · see citing works
Maynard, K. R., Tippani, M., Takahashi, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/781559 · Cited by 12 · see citing works
Self-citationDong, X., Shi, M. K., Lee, M., Toro, R., Gravina, S., Han, W., Yasuda, S., Wang, T., Zhang, Z., Vijg, J., et al. (2018) · Epigenetics · doi:10.1080/15592294.2018.1441650 · Cited by 10 · see citing works
Lun, A. (2019) · NCI BioConductor · doi:10.18129/b9.bioc.biocsingular · Cited by 3 · see citing works
LieberInstitute/HumanPilot: Archive the HumanPilot code for the preprint version of our project
Collado-Torres, L., Weber, L., & Hicks, S. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3691916 · Cited by 3 · see citing works
Self-citationAn Organ-Wide Gene Expression Atlas of the Developing Human HeartPreprint
Asp, M., Giacomello, S., Fürth, D., Reimegård, J., Wärdell, E., Custodio, J., Salmén, F., Sundström, E., Åkesson, E., Bienko, M., et al. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3219263 · Cited by 2 · see citing works
Jaffe, A. E., Hoeppner, D., Saito, T., Blanpain, L. T., Ukaigwe, J., Burke, E., Tao, R., Tajinda, K., Deep‐Soboslay, A., Shin, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/612200 · Cited by 2 · see citing works
Self-citationLieberInstitute/spatialLIBD: spatialLIBD: initial Bioconductor submission
Collado-Torres, L., Pardo, B., Abspangler13, et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3689719 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113 · see citing works
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700 · see citing works
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459 · see citing works
Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders
Brandebura, A. N., Paumier, A., Onur, T. S., & Allen, N. J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00641-1 · Cited by 427 · see citing works
Sadick, J. S., O’Dea, M. R., Hasel, P., Dykstra, T., Faustin, A., & Liddelow, S. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.008 · Cited by 415 · see citing works
Alignment and integration of spatial transcriptomics data
Zeira, R., Land, M., Strzalkowski, A., & Raphael, B. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01459-6 · Cited by 298 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282 · see citing works
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225 · see citing works
Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211 · see citing works
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219 · see citing works
Self-citationSpatial transcriptomics: Technologies, applications and experimental considerations
Wang, Y., Liu, B., Zhao, G., Lee, Y., Buzdin, A., Mu, X., Zhao, J., Chen, H., & Li, X. (2023) · Genomics · doi:10.1016/j.ygeno.2023.110671 · Cited by 218 · see citing works
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“It has been commercialized and extensively employed across diverse fields such as developmental biology [75], cancer biology [76], as well as neuroscience [77, 78].”
BackgroundAnalyzing microglial phenotypes across neuropathologies: a practical guide
Schwabenland, M., Brück, W., Priller, J., Stadelmann, C., Lassmann, H., & Prinz, M. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02370-8 · Cited by 149 · see citing works
Han, B., Zhou, S., Zhang, Y., Chen, S., Xi, W., Liu, C., Zhou, X., Yuan, M., Yu, X., Li, L., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adg1323 · Cited by 143 · see citing works
Xu, P., Wang, M., Song, W.-M., Wang, Q., Yuan, G.-C., Sudmant, P. H., Zare, H., Tu, Z., Orr, M. E., & Zhang, B. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00507-7 · Cited by 114 · see citing works
Review of multi-omics data resources and integrative analysis for human brain disorders
Dong, X., Liu, C., & Dozmorov, M. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab024 · Cited by 47 · 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
From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies
Sharma, A., Raut, S. S., Shukla, A., Gupta, S., Mishra, A., & Singh, A. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152389 · Cited by 29 · see citing works
Ma, C., Chitra, U., Zhang, S., & Raphael, B. J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.002 · Cited by 27 · see citing works
Uncovering Plaque-Glia Niches in Human Alzheimer’s Disease Brains Using Spatial Transcriptomics
Avey, D. R., Ng, B., Vialle, R. A., Kearns, N. A., de Paiva Lopes, K., Iatrou, A., De Tissera, S., Vyas, H., Saunders, D. M., Flood, D. J., et al. (2024) · doi:10.1101/2024.09.05.611566 · Cited by 24
MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics
Yang, Y., Zhang, C., Liu, Z., Aihara, K., Zhang, C., Chen, L., & Wei, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae608 · Cited by 5 · see citing works
Distinct Cellular Phenotypes of Language and Executive Decline in Amyotrophic Lateral Sclerosis
Petrescu, J., Gouveia Roque, C., Jackson, C. A., Daly, A. C., Butti, Z., Kang, K., Casel, O., Leung, M., Reilly, L., Eschbach, J., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.008 · Cited by 4 · see citing works
Uncovering the signatures of aging and senescence in the human dorsolateral prefrontal cortex
Sloan, N. X., Mares, J., Daly, A. C., Grier, S., Haq, I., Jackson, C. A., Barretto, N., Casel, O., Kang, K., Khiste, S., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101127 · Cited by 2 · see citing works
“Importantly, we found that layer marker distributions within our AARs agreed with the biology described in the literature, 11,48 with RELN displaying the highest level of expression in annotated layer 1, CARTPT in layers 2 and 3, RORB in layer 4, PCP4 in layers 4 and 5, and MOBP in the white matter…”
- Authors
- Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., Chew, J., Yin, Y., Kleinman, J. E., Hyde, T. M., Rao, N., Hicks, S. C., Martinowich, K., & Jaffe, A. E.
- Affiliations
- Johns Hopkins University (US)
- 10X Genomics (United States) (US)
- Lieber Institute for Brain Development (US)
- Corresponding author
- Martinowich, Keri
- Jaffe, Andrew E.
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2021-02-08
- Volume
- 24
- Issue
- 3
- Pages
- 425-436
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC8095368
- OpenAlex
- W3126958162
- Semantic Scholar
- 86ad0695623e8964d06d97dd88b8958b566751de
- Cited by
- 1,222
- References
- 91
- Citations by year
- 2026: 283 · 2025: 297 · 2024: 274 · 2023: 191 · 2022: 124 · 2021: 44 · 2020: 9
- FWCI
- 50.96
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-02-18)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (U01MH122849)
- Lieber Institute for Brain Development
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01CA237170)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R00HG009007)
- Chan Zuckerberg Initiative DAF
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- dorsolateral prefrontal cortex, spatial gene expression, cortical laminar organization, single-nucleus RNA sequencing, schizophrenia, autism spectrum disorder, human brain transcriptome
- MeSH terms
- Humans, Gene Expression, Prefrontal Cortex, Gene Regulatory Networks, 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
