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Spatial reconstruction of single-cell gene expression data
Open accessAbstract
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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In the citing paper
“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood35.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [35]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Batch effects were corrected with the FindIntegrationAnchor() and the IntegrateData() function in the reciprocal PCA approach of the Seurat workflow.”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [76]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Critically, such methods can only detect variations that are captured by differences between discrete groups.”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [3]
Experimental — accuracy not yet measuredIn the citing paper
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“even when a cell population partitions into discrete types, hybrid cells—single cells that are a mix of two or more types—may be observed95,97”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“One approach used existing in situ hybridisation maps of spatially restricted genes as a “barcode” to which the complete expression profiles of individual cells can be matched.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“This was applied by two groups to reconstruct expression patterns in zebrafish embryos (Satija et al, 2015), and to the brain of the marine annelid Platynereis dumerilii (Achim et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“Notably, the down-regulation of ribosomal genes and bulk protein synthesis has been long considered as a hallmark of aging 54,55 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
In the citing paper
“Thus, while there may be hallmarks of aging that occur in most cell types, such as mitochondrial dysfunction and loss of proteostasis 1,4,54 , our data argue against the hypothesis that aging induces a single universal molecular program in all cells and tissues 16 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · cited 1×
“Specifically, Seurat uses the expression levels of all highly variable genes in the RNA-seq data set and an L1-constrained, LASSO (least absolute shrinkage and selection operator 23 ) technique to construct separate models of gene expression for each of the landmark genes.”
MethodStages of embryonic development of the zebrafish
Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., & Schilling, T. F. (1995) · Developmental Dynamics · doi:10.1002/aja.1002030302 · Cited by 12,037 · cited 1×
“Finally, we observed a population of 94 cells expressing high levels of canonical markers of the enveloping layer (e.g., krt18 , krt4 , cldne ), a single layer of differentiated squamous cells that cover the outside of the embryo 30,31 .”
BackgroundTrapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · cited 1×
“” Nature biotechnology 33 (5): 495-502.”
The developmental transcriptome of Drosophila melanogaster
Graveley, B. R., Brooks, A. N., Carlson, J. W., Duff, M. O., Landolin, J. M., Yang, L. C., Artieri, C. G., van Baren, M. J., Boley, N. P., Booth, B. W., et al. (2010) · Nature · doi:10.1038/nature09715 · Cited by 1,504 · cited 8×
“Seurat consists of the following steps: (1) It uses co-expression patterns across cells in the single-cell RNA-seq profiles to impute the expression of each landmark gene in each cell.”
BackgroundMethodInfluentialmixtools : An R Package for Analyzing Finite Mixture Models
Benaglia, T., Chauveau, D., Hunter, D. R., & Young, D. S. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i06 · Cited by 1,391
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · cited 3×
“Single-cell measurements for a single gene, however, are inherently noisy, with extensive variability stemming from both biological and technical sources 21,22 .”
BackgroundSingle-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345 · cited 1×
“Single-cell RNA-seq is quickly becoming an established experimental method, with ongoing cost and throughput improvements enabling applications from cell type discovery 6,7 to regulatory network inference 8,9 to the reconstruction of developmental processes 10–12 .”
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Li, S., Ling, S., Wang, D., Wang, X., Hao, F., Yin, L., Yuan, Z., Liu, L., Zhang, L., Li, Y., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.04.021 · Cited by 87
Huang, D., Guo, G., Yuan, P., Ralston, A., Sun, L., Huss, M., Mistri, T. K., Pinello, L., Ng, H., Yuan, G., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16009-w · Cited by 82
Bayesian sparse regression analysis documents the diversity of spinal inhibitory interneurons
Gabitto, M. I., Pakman, A., Bikoff, J. B., Abbott, L. F., Jessell, T. M., & Paninski, L. (2016) · Cell · doi:10.1016/j.cell.2016.01.026 · Cited by 73
Li, G., Tian, L., Goodyer, W. R., Kort, E. J., Buikema, J. W., Xu, A., Wu, J. C., Jovinge, S. V., & Wu, S. M. (2019) · Development · doi:10.1242/dev.173476 · Cited by 79
Semisoft clustering of single-cell data
Zhu, L., Lei, J., Klei, L., Devlin, B., & Roeder, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817715116 · Cited by 81
Defining Transcriptional Signatures of Human Hair Follicle Cell States
Takahashi, R., Grzenda, A. L., Allison, T. F., Rawnsley, J. D., Balin, S. J., Sabri, S., Plath, K., & Lowry, W. E. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.07.726 · Cited by 81
Tumor diversity and the trade-off between universal cancer tasks
Hausser, J., Székely, P., Bar, N., Zimmer, A., Sheftel, H., Caldas, C., & Alon, U. (2019) · Nature Communications · doi:10.1038/s41467-019-13195-1 · Cited by 78
Single-cell transcriptome analysis identifies skin-specific T-cell responses in systemic sclerosis
Gaydosik, A. M., Tabib, T., Domsic, R. T., Khanna, D., Lafyatis, R., & Fuschiotti, P. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2021-220209 · Cited by 85
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Rochman, M., Wen, T., Kotliar, M., Dexheimer, P. J., Morgenstern, N. B.-B., Caldwell, J. M., Lim, H., & Rothenberg, M. E. (2022) · JCI Insight · doi:10.1172/jci.insight.159093 · Cited by 81
“Cell cycle analysis using Seurat revealed the presence of genes from the full spectrum of the cell cycle: G1, S, and G2M.”
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Sario, G. D., Rossella, V., Famulari, E. S., Maurizio, A., Lazarević, D., Giannese, F., & Felici, C. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1152470 · Cited by 88
“Therefore, even if the bioinformatics pipelines currently used for CTC derived bulk and scRNAseq sequencing data are the same used for other types of transcriptomic data (Satija et al., 2015; Wolf et al., 2018) analyses of these samples require additional quality evaluation and preprocessing.”
MethodGe, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 97
Behavior-related gene regulatory networks: A new level of organization in the brain
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PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq
Tyler, S. R., Rotti, P. G., Sun, X., Yi, Y., Xie, W., Winter, M. C., Flamme‐Wiese, M. J., Tucker, B. A., Mullins, R. F., Norris, A. W., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.063 · Cited by 77
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Atkins, M. H., Scarfò, R., McGrath, K. E., Yang, D., Palis, J., Ditadi, A., & Keller, G. M. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20211924 · Cited by 78
“2 R package (Hafemeister and Satija, 2019; Macosko et al., 2015; Satija et al., 2015), as described in the tutorials (http://satijalab.”
MethodscDFC: A deep fusion clustering method for single-cell RNA-seq data
Hu, D., Liang, K., Zhou, S., Tu, W., Liu, M., & Liu, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad216 · Cited by 83
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 90
Microglia-astrocyte crosstalk regulates synapse remodeling via Wnt signaling
Faust, T. E., Lee, Y., O’Connor, C. D., Boyle, M. A., Gunner, G., Durán‐Laforet, V., Ferrari, L. L., Murphy, R. E., Badimon, A., Sakers, K., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.023 · Cited by 93
Immunology in the Era of Single-Cell Technologies
Efremova, M., Vento‐Tormo, R., Park, J.-E., Teichmann, S. A., & James, K. R. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090419-020340 · Cited by 80
bigSCale: an analytical framework for big-scale single-cell data
Iacono, G., Mereu, E., Guillaumet-Adkins, A., Corominas, R., Cuscó, I., Rodríguez-Esteban, G., Gut, M., Pérez-Jurado, L. A., Gut, I., & Heyn, H. (2018) · Genome Research · doi:10.1101/gr.230771.117 · Cited by 79
DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
Sun, Z., Wang, T., Deng, K., Wang, X., Lafyatis, R., Ding, Y., Hu, M., & Chen, W. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx490 · Cited by 76
Tamburini, B. A. J., Finlon, J. M., Gillen, A. E., Kriss, M. S., Riemondy, K. A., Fu, R., Schuyler, R. P., Hesselberth, J. R., Rosen, H. R., & Burchill, M. A. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01036 · Cited by 76
Afik, S., Yates, K. B., Bi, K., Darko, S. W., Godec, J., Gerdemann, U., Swadling, L., Douek, D. C., Klenerman, P., Barnes, E. J., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx615 · Cited by 82
Mitogenic and progenitor gene programmes in single pilocytic astrocytoma cells
Reitman, Z. J., Paolella, B. R., Bergthold, G., Pelton, K., Becker, S., Jones, R. E., Sinai, C. E., Malkin, H., Huang, Y., Grimmett, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11493-2 · Cited by 78
Self-citationBöttcher, A., Büttner, M., Tritschler, S., Sterr, M., Aliluev, A., Oppenländer, L., Burtscher, I., Sass, S., Irmler, M., Beckers, J., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-020-00617-2 · Cited by 80
Expansion of interferon inducible gene pool via USP18 inhibition promotes cancer cell pyroptosis
Arimoto, K., Miyauchi, S., Troutman, T. D., Zhang, Y., Liu, M., Stoner, S. A., Davis, A. G., Fan, J.-B., Huang, Y.-J., Yan, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35348-5 · Cited by 83
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71
“Other methods formulate complete probabilistic inference framework building models of gene expression for the measured landmark genes in each bin or layer to infer the cell’s spatial location [61, 63].”
MethodSpatiotemporal Patterning of Zygotic Genome Activation in a Model Vertebrate Embryo
Chen, H., Einstein, L. C., Little, S. C., & Good, M. C. (2019) · Developmental Cell · doi:10.1016/j.devcel.2019.05.036 · Cited by 75
Kelly, N. H., Huynh, N. P. T., & Guilak, F. (2019) · Matrix Biology · doi:10.1016/j.matbio.2019.12.004 · Cited by 77
Reustle, A., Marco, M. D., Meyerhoff, C., Nelde, A., Walz, J. S., Winter, S., Kandabarau, S., Büttner, F., Haag, M., Backert, L., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00731-8 · Cited by 78
An Activity-Mediated Transition in Transcription in Early Postnatal Neurons
Stroud, H., Yang, M. G., Tsitohay, Y. N., Davis, C. P., Sherman, M. A., Hrvatin, S., Ling, E., & Greenberg, M. E. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.008 · Cited by 78
“8 Huang de et al., 2009 https://david.ncifcrf.gov/ Tophat2 Kim et al., 2013 https://ccb.ihu.edu/software/ tophat/index.shtml HTseq Anders et al., 2015 https://pvpi.Pvthon.ora/ cvpi/HTSea DEseq Anders et al., 2015 http://bioconductor.ora/ cackaaes/release/bioc/html/ DESea.html Seurat Satija et al., 2015 http://satiiala…”
MethodSingle-Cell RNA-Sequencing From Mouse Incisor Reveals Dental Epithelial Cell-Type Specific Genes
Chiba, Y., Saito, K., Martı́n, D., Boger, E. T., Rhodes, C. S., Yoshizaki, K., Nakamura, T., Yamada, A., Morell, R. J., Yamada, Y., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00841 · Cited by 78
“Cell Clustering Analyses Secondary analysis and filtering were performed using the Seurat v2 R package (Satija et al., 2015).”
MethodMolecular identity of human limbal heterogeneity involved in corneal homeostasis and privilege
Dou, S., Wang, Q., Qi, X., Zhang, B., Jiang, H., Chen, S., Duan, H., Lu, Y., Dong, J., Cao, Y., et al. (2021) · The Ocular Surface · doi:10.1016/j.jtos.2021.04.010 · Cited by 79
Lipid droplet dynamics regulate adult muscle stem cell fate
Yue, F., Oprescu, S. N., Qiu, J., Gu, L., Zhang, L., Chen, J., Narayanan, N., Deng, M., & Kuang, S. (2022) · Cell Reports · doi:10.1016/j.celrep.2021.110267 · Cited by 80
Vu, T., Vallmitjana, A., Gu, J., La, K., Xu, Q., Flores, J., Zimak, J., Shiu, J., Hosohama, L., Wu, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27798-0 · Cited by 80
“samples (overlaid lines correspond to quantiles [10,50,90]%, n=364 cells).”
BackgroundO'Toole, S. M., Oyibo, H. K., & Keller, G. B. (2023) · Neuron · doi:10.1016/j.neuron.2023.08.015 · Cited by 103
“61,62 Initially, 2000 genes were selected and mitochondrial percentage, sample effects, 10x chemistry (v2 vs. v3), and total feature counts were regressed out.”
Wang, Q., Wang, M., Choi, I., Sarrafha, L., Liang, M., Ho, L., Farrell, K. W., Beaumont, K. G., Sebra, R., Sanctis, C. D., et al. (2024) · Science Advances · doi:10.1126/sciadv.adi8287 · Cited by 88
“DEGs between PD and control and DEGs between different Braak stages and control in each cluster/subcluster were identified using the R package MAST ( 19 , 63 ) implemented in Seurat, with correction for PMI, age, and sex.”
MethodDifferentiation route determines the functional outputs of adult megakaryopoiesis
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Hwang, S., Awasthi, D., Jeong, J., Sandoval, T. A., Chae, C.-S., Ramos, Y., Chen, T., Falco, M. M., Salvagno, C., Emmanuelli, A., et al. (2024) · Nature · doi:10.1038/s41586-024-08071-y · Cited by 83
“0.2), Seurat R Package (v3.”
InfluentialTargeted Suppression of miRNA-33 Using pHLIP Improves Atherosclerosis Regression
Zhang, X., Rotllan, N., Canfrán-Duque, A., Sun, J., Toczek, J., Moshnikova, A., Malik, S., Price, N. L., Araldi, E., Zhong, W., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.320296 · Cited by 75
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Erwin, S. R., Sun, W., Copeland, M., Lindo, S. E., Spruston, N., & Cembrowski, M. S. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107551 · Cited by 82
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Howard, A. G. A., Baker, P. A., Ibarra‐García‐Padilla, R., Moore, J. A., Rivas, L. J., Tallman, J. J., Singleton, E. W., Westheimer, J. L., Corteguera, J. A., & Uribe, R. A. (2021) · eLife · doi:10.7554/elife.60005 · Cited by 76
“At 68-70 hpf, we confirmed the four distinct pigment populations we identified through Seurat (Fig.”
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Guo, W., Zhou, B., Yang, Z., Liu, X., Huai, Q., Guo, L., Xue, X., Tan, F., Li, Y., Xue, Q., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104281 · Cited by 76
CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis
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“Using Seurat (4.1.1) 52,53 and R (4.2.1), in-house datasets were additionally, before quality control, subjected to hashtag antibody oligonucleotide (HTO) separation using Seurat’s ‘HTODe-mux’ with a 0.99 threshold, to separate replicates or conditions and to remove doublets and negative cells.”
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Cellular and molecular properties of neural progenitors in the developing mammalian hypothalamus
Zhou, X., Zhong, S., Peng, H., Liu, J., Ding, W., Sun, L., Ma, Q., Liu, Z., Chen, R., Wu, Q., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17890-2 · Cited by 74
Molecular Biomarkers for Celiac Disease: Past, Present and Future
Ramírez-Sánchez, A. D., Tan, I. L., Jong, B. C. G.-D., Visschedijk, M. C., Jonkers, I. H., & Withoff, S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21228528 · Cited by 82
“…examples of multilayer techniques include the characterization of cell surface markers (CITE-seq [137]), whole genome screening of open (active) chromatin (single-cell RNA / ATAC-seq [138]), actual position in the tissue by spatial transcriptome reconstruction, or mass cytometry imaging [139–141].”
Nayler, S., Agarwal, D., Curion, F., Bowden, R. J., & Becker, E. B. E. (2021) · Scientific Reports · doi:10.1038/s41598-021-91846-4 · Cited by 76
Yu, H., Sun, Z., Tan, T., Pan, H., Zhao, J., Zhang, L., Chen, J., Lei, A., Zhu, Y., Chen, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26576-2 · Cited by 76
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
Cao, S., Wang, J. R., Ji, S., Peng, Y., Dai, Y., Guo, S., Montierth, M. D., Shen, J. P., Zhao, X. H., Chen, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01342-x · Cited by 81
Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq
Li, Q., Wang, R., Yang, Z., Li, W., Yang, J., Wang, Z., Bai, H., Cui, Y., Tian, Y., Wu, Z., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01089-9 · Cited by 82
“2) [37] was used to identify differentially expressed genes (DEGs) among several groups with parameters (logfc.”
MethodWang, Z., Wang, Y., Chang, M., Wang, Y., Liu, P., Wu, J., Wang, G., Tang, X., Hui, X., Liu, P., et al. (2023) · Neuro-Oncology · doi:10.1093/neuonc/noad017 · Cited by 87
Chen, Z., Li, Y., Yuan, Y., Lai, K., Ye, K., Lin, Y., Lan, R., Chen, H., & Xu, Y. (2023) · Cell Death and Disease · doi:10.1038/s41419-023-05830-z · Cited by 82
Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data
Menon, V. (2017) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx044 · Cited by 73
An integrated single-cell transcriptomic dataset for non-small cell lung cancer
Prazanowska, K. H., & Lim, S. B. (2023) · Scientific Data · doi:10.1038/s41597-023-02074-6 · Cited by 75
Golomb, S. M., Guldner, I. H., Zhao, A., Wang, Q., Palakurthi, B., Aleksandrovic, E., Lopez, J. A., Lee, S. W., Yang, K., & Zhang, S. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108438 · Cited by 77
A RIPK1-regulated inflammatory microglial state in amyotrophic lateral sclerosis
Mifflin, L., Hu, Z., Dufort, C., Hession, C. C., Walker, A. J., Niu, K., Zhu, H., Liu, N., Liu, J. S., Levin, J. Z., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025102118 · Cited by 79
“5 mo including two WT vehicle-treated, two SOD1 vehicle-treated, and two SOD1 Nec-1s-treated using the Seurat package for unsupervised clustering (30, 31).”
MethodClump sequencing exposes the spatial expression programs of intestinal secretory cells
Manco, R., Averbukh, I., Porat, Z., Halpern, K. B., Amit, I., & Itzkovitz, S. (2021) · Nature Communications · doi:10.1038/s41467-021-23245-2 · Cited by 69
Characterization of human FDCs reveals regulation of T cells and antigen presentation to B cells
Heesters, B. A., van Megesen, K., Tomris, I., de Vries, R. P., Magri, G., & Spits, H. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210790 · Cited by 72
Crafting a blueprint for single-cell RNA sequencing
Denyer, T., & Timmermans, M. C. P. (2021) · Trends in Plant Science · doi:10.1016/j.tplants.2021.08.016 · Cited by 76
Updates on Immunotherapy and Immune Landscape in Renal Clear Cell Carcinoma
Kim, M., Jin, Z., Kolb, R. H., Borcherding, N. C., Chatzkel, J. A., Falzarano, S. M., & Zhang, W. (2021) · Cancers · doi:10.3390/cancers13225856 · Cited by 80
Sheerin, D., Abhimanyu, A., Peton, N., Vo, W., Allison, C. C., Wang, X., Johnson, W. E., & Coussens, A. K. (2022) · iScience · doi:10.1016/j.isci.2022.104464 · Cited by 78
SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Zhu, J., Shang, L., & Zhou, X. (2023) · Genome biology · doi:10.1186/s13059-023-02879-z · Cited by 73
“…that include five methods originally developed for spatial clustering (BayesSpace [59], stLearn with Kmeans, stLearn with Louvain [36], SpaGCN [39], HMRF [37, 38]), and one method originally developed for non-spatial clustering but previously used for spatial clustering (SNN in Seurat [35, 60]).”
MethodZebrafish pigment cells develop directly from persistent highly multipotent progenitors
Subkhankulova, T., Sosa, K. C., Урошлев, Л. А., Nikaido, M., Shriever, N. P., Kasianov, A. S., Yang, X., Rodrigues, F. S. L. M., Carney, T. J., Bavister, G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36876-4 · Cited by 105
A spatial transcriptome map of the developing maize ear
Wang, Y., Luo, Y., Guo, X., Li, Y., Yan, J., Shao, W., Wei, W., Wei, X., Yang, T., Chen, J. W., et al. (2024) · Nature Plants · doi:10.1038/s41477-024-01683-2 · Cited by 77
“Differentially expressed genes were then identified using the FindAllMarkers function in the R package Seurat (min.pct, 0.5; log2fc. threshold, 1) and filtered by p_val_adj < 0.01.”
InfluentialJha, R. M., Rajasundaram, D., Sneiderman, C. T., Schlegel, B. T., O’Brien, C., Xiong, Z., Janesko‐Feldman, K. L., Trivedi, R., Vagni, V., Zusman, B. E., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.06.021 · Cited by 82
An early precursor CD8+ T cell that adapts to acute or chronic viral infection
McManus, D. T., Valanparambil, R. M., Medina, C. B., Scharer, C. D., McGuire, D. J., Sobierajska, E., Hu, Y., Chang, D. Y., Wieland, A., Lee, J., et al. (2025) · Nature · doi:10.1038/s41586-024-08562-y · Cited by 76
“Further data analyses was performed using Seurat (v.3.0) 39 .”
InfluentialTargeting phenotypic heterogeneity in benign prostatic hyperplasia
Strand, D. W., da Costa, D. N., Francis, F., Ricke, W. A., & Roehrborn, C. G. (2017) · Differentiation · doi:10.1016/j.diff.2017.07.005 · Cited by 77
A unified statistical framework for single cell and bulk RNA sequencing data
Zhu, L., Lei, J., Devlin, B., & Roeder, K. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1110 · Cited by 78
Single-cell RNA-seq clustering: datasets, models, and algorithms
Peng, L., Tian, X., Tian, G., Xu, J., Huang, X., Weng, Y., Yang, J., & Zhou, L. (2020) · RNA Biology · doi:10.1080/15476286.2020.1728961 · Cited by 77
Khazaei, S., Jay, N. D., Deshmukh, S., Hendrikse, L. D., Jawhar, W., Chen, C. C. L., Mikael, L. G., Faury, D., Marchione, D. M., Lanoix, J., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0461 · Cited by 76
“Low-quality cells were excluded based on outlier mitochondrial content (indicative of cellular stress or damage) and number of genes expressed using the R package Seurat (RRID: SCR_016341) (57,58) (v3.”
MethodLeach, S. M., Gibbings, S. L., Tewari, A., Atif, S. M., Vestal, B. E., Danhorn, T., Janssen, W. J., Wager, T. D., & Jakubzick, C. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108337 · Cited by 74
Single-cell dual-omics reveals the transcriptomic and epigenomic diversity of cardiac non-myocytes
Wang, L., Yang, Y., Ma, H., Xie, Y., Xu, J., Near, D., Wang, H., Garbutt, T. A., Li, Y., Liu, J., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab134 · Cited by 73
Subcellular localization of biomolecules and drug distribution by high-definition ion beam imaging
Rovira‐Clavé, X., Jiang, S., Bai, Y., Zhu, B., Barlow, G. L., Bhate, S. S., Coskun, A. F., Han, G., Ho, C. K., Hitzman, C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24822-1 · Cited by 76
scNAME: neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data
Wan, H. S., Chen, L., & Deng, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac011 · Cited by 75
Chen, Y., Yu, Y., Wen, Y., Chen, J., Lin, J., Sheng, Z., Zhou, W., Sun, H., An, C., Chen, J., et al. (2022) · Bone Research · doi:10.1038/s41413-022-00209-w · Cited by 76
“ScRNA-seq identifies distinct cell subpopulations in the early and late passages Next, we analyzed chondrocyte subpopulations in P0–8 cells using Seurat unsupervised clustering from the R package.(36) Four chondrocyte clusters were distinctly classified (Fig.”
MethodBenchmarking cell-type clustering methods for spatially resolved transcriptomics data
Cheng, A., Hu, G., & Li, W. V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac475 · Cited by 75
“Seurat [32] is an R toolkit for single-cell genomic data analysis and provides methods for dimensionality reduction and clustering of spatial transcriptomics data.”
MethodSingle‐cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment
Cao, G., Yue, J., Ruan, Y., Han, Y., Zhi, Y., Lu, J., Liu, M., Xu, X., Wang, J., Gu, Q., et al. (2023) · The EMBO Journal · doi:10.15252/embj.2022110757 · Cited by 75
CD4 + T cell immunity against cutaneous melanoma encompasses multifaceted MHC II–dependent responses
Bawden, E., Wagner, T., Schröder, J., Effern, M., Hinze, D., Newland, L., Attrill, G. H., Lee, A. R., Engel, S., Freestone, D., et al. (2024) · Science Immunology · doi:10.1126/sciimmunol.adi9517 · Cited by 76
Application of single-cell genomics in cancer: promise and challenges
Wills, Q. F., & Mead, A. J. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv235 · Cited by 77
Kalish, B. T., Cheadle, L., Hrvatin, S., Nagy, M. A., Rivera, S. A., Crow, M., Gillis, J., Kirchner, R., & Greenberg, M. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717871115 · Cited by 70
The Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 86
Self-citationSingle-cell longitudinal analysis of SARS-CoV-2 infection in human airway epitheliumPreprint
Ravindra, N. G., Alfajaro, M. M., Gasque, V., Habet, V., Wei, J., Filler, R. B., Huston, N. C., Wan, H., Szigeti-Buck, K., Wang, B., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.06.081695 · Cited by 76
Davies, A. E., Pargett, M., Siebert, S., Gillies, T. E., Choi, Y., Tobin, S. J., Ram, A. R., Murthy, V., Juliano, C. E., Quon, G., et al. (2020) · Cell Systems · doi:10.1016/j.cels.2020.07.004 · Cited by 71
Targeting PIM1-Mediated Metabolism in Myeloid Suppressor Cells to Treat Cancer
Xin, G., Yao, C., Topchyan, P., Kasmani, M. Y., Burns, R., Volberding, P. J., Wu, X., Cohn, A., Chen, Y., Lin, C., et al. (2021) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-20-0433 · Cited by 73
Feyen, D. A. M., Perea‐Gil, I., Maas, R. G. C., Harakaľová, M., Gavidia, A. A., Ataam, J. A., Wu, T.-H., Vink, A., Pei, J., Vadgama, N., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.049844 · Cited by 73
Mende, N., Bastos, H. P., Santoro, A., Mahbubani, K. T. A., Ciaurro, V., Calderbank, E. F., Londoño, M. Q., Sham, K., Mantica, G., Morishima, T., et al. (2022) · Blood · doi:10.1182/blood.2021013450 · Cited by 77
Discovery and validation of human genomic safe harbor sites for gene and cell therapies
Aznauryan, E. I., Yermanos, A., Kinzina, E., Devaux, A., Kapetanovic, E., Milanova, D., Church, G. M., & Reddy, S. T. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100154 · Cited by 77
Bondeson, D. P., Paolella, B. R., Asfaw, A., Rothberg, M. V., Skipper, T. A., Langan, C. R., Mesa, G., Gonzalez, A. E., Surface, L. E., Ito, K., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00360-7 · Cited by 74
A Targetable EGFR-Dependent Tumor-Initiating Program in Breast Cancer
Savage, P., Blanchet-Cohen, A., Revil, T., Badescu, D., Saleh, S. M. I., Wang, Y.-C., Zuo, D., Liu, L., Bertos, N., Muñoz-Ramos, V., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.015 · Cited by 74
Single-Cell Transcriptomics Reveals Chemotaxis-Mediated Intraorgan Crosstalk During Cardiogenesis
Xiong, H., Luo, Y., Yue, Y., Zhang, J., Ai, S., Li, X., Wang, X., Zhang, Y.-L., Wei, Y., Li, H., et al. (2019) · Circulation Research · doi:10.1161/circresaha.119.315243 · Cited by 71
BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization
Taverna, F., Goveia, J., Karakach, T. K., Khan, S., Rohlenová, K., Treps, L., Subramanian, A., Schoonjans, L., Dewerchin, M. A., Eelen, G., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa332 · Cited by 72
“Also, BIOMEX supports K-means, hierarchical and graph-based clustering (Seurat (21) and FlowSOM (28) packages).”
Method
Metadata
- Authors
- Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Schier, Alexander F
- Regev, Aviv
- Venue
- Nature Biotechnology
- Published
- 2015-04-13
- Volume
- 33
- Issue
- 5
- Pages
- 495-502
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4430369
- OpenAlex
- W2069089843
- Cited by
- 7,055
- References
- 50
- Citations by year
- 2026: 931 · 2025: 1220 · 2024: 1142 · 2023: 987 · 2022: 784 · 2021: 640 · 2020: 510 · 2019: 341 · 2018: 278 · 2017: 126 · 2016: 64 · 2015: 27
- FWCI
- 71.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- edoc (University of Basel) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Jane Coffin Childs Memorial Fund for Medical Research
- National Institutes of Health
- Klarman Cell Observatory, Broad Institute
- National Institute of Mental Health (U01MH105960)
- National Human Genome Research Institute (P50HG006193)
- National Institute of General Medical Sciences (R01GM056211)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial reconstruction, single-cell RNA sequencing, subcellular localization, spatial transcriptomics, zebrafish embryos, spatial patterning, spatial markers, Seurat
- MeSH terms
- Animals, Image Processing, Computer-Assisted, Zebrafish, In Situ Hybridization, Fluorescence, Gene Expression Regulation, Developmental, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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