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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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Cites
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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Aryl hydrocarbon receptor Controls Monocyte Differentiation into dendritic Cells versus Macrophages
Goudot, C., Coillard, A., Villani, A.-C., Gueguen, P., Cros, A., Sarkizova, S., Tang-Huau, T.-L., Bohec, M., Baulande, S., Hacohen, N., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.08.016 · Cited by 382
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Shi, Y., Sun, L., Liu, J., Zhong, S., Wang, M., Li, R., Li, P., Guo, L., Fang, A., Chen, R., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000705 · Cited by 412
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Zhang, Y., Wang, D., Miao, P., Tang, L. M., Ouyang, J., Xiong, F., Guo, C. J., Tang, Y., Zhou, Y., Liao, Q., et al. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01874-1 · Cited by 412
Embracing the dropouts in single-cell RNA-seq analysis
Qiu, P. (2020) · Nature Communications · doi:10.1038/s41467-020-14976-9 · Cited by 411
Wang, Y., Song, W., Wang, J., Wang, T., Xiong, X., Qi, Z., Fu, W., Yang, X., & Chen, Y.-G. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20191130 · Cited by 399
Androgen receptor activity in T cells limits checkpoint blockade efficacy
Guan, X., Polesso, F., Wang, C., Sehrawat, A., Hawkins, R. M., Murray, S., THOMAS, G. V., Caruso, B., Thompson, R. F., Wood, M. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04522-6 · Cited by 408
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Fan, J., Salathia, N. S., Liu, R., Kaeser, G. E., Yung, Y. C., Herman, J. L., Kaper, F., Fan, J., Zhang, K., Chun, J. J. M., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3734 · Cited by 376
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Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 382
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Nam, A. S., Chaligné, R., & Landau, D. A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0265-5 · Cited by 399
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Jin, H., Li, M., Jeong, E., Castro-Martinez, F., & Zuker, C. S. (2024) · Nature · doi:10.1038/s41586-024-07469-y · Cited by 383
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Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018) · GigaScience · doi:10.1093/gigascience/giy059 · Cited by 364
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Sun, W., Dong, H., Baláž, M., Slyper, M., Drokhlyansky, E., Colleluori, G., Giordano, A., Kovaničová, Z., Štefanička, P., Balážová, L., et al. (2020) · Nature · doi:10.1038/s41586-020-2856-x · Cited by 363
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Siebert, S., Farrell, J. A., Cazet, J. F., Abeykoon, Y., Primack, A. S., Schnitzler, C. E., & Juliano, C. E. (2019) · Science · doi:10.1126/science.aav9314 · Cited by 360
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Borcherding, N. C., Vishwakarma, A., Voigt, A. P., Bellizzi, A. M., Kaplan, J., Nepple, K. G., Salem, A. K., Jenkins, R. W., Zakharia, Y., & Zhang, W. (2021) · Communications Biology · doi:10.1038/s42003-020-01625-6 · Cited by 379
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Herman, J. S., Sagar, & Grün, D. (2018) · Nature Methods · doi:10.1038/nmeth.4662 · Cited by 363
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Breschi, A., Gingeras, T., & Guigó, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.19 · Cited by 389
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Yan, K. S., Gevaert, O., Zheng, G. X. Y., Anchang, B., Probert, C. S., Larkin, K. A., Davies, P. S., Cheng, Z., Kaddis, J. S., Han, A. S., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.06.014 · Cited by 364
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Lee, J., Tammela, T., Hofree, M., Choi, J., Marjanovic, N. D., Han, S., Canner, D. A., Wu, K., Paschini, M., Bhang, D. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.028 · Cited by 363
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Fabre, T., Barron, A. M. S., Christensen, S. M., Asano, S., Bound, K., Lech, M. P., Wadsworth, M. H., Chen, X., Wang, C., Wang, J., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.add8945 · Cited by 355
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Spatiotemporal single-cell analysis of gene expression in the mouse suprachiasmatic nucleus
Wen, S., Ma, D., Zhao, M., Xie, L., Wu, Q., Gou, L., Zhu, C., Fan, Y., Wang, H., & Yan, J. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0586-x · Cited by 328
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Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357
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Chen, J., Suo, S., Tam, P. P., Han, J.-D. J., Peng, G., & Jing, N. (2017) · Nature Protocols · doi:10.1038/nprot.2017.003 · Cited by 339
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Mitsialis, V., Wall, S. M., Liu, P., Ordovás-Montañés, J., Parmet, T., Vukovic, M., Spencer, D., Field, M., McCourt, C. C., Toothaker, J. M., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.04.074 · Cited by 354
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Tabib, T., Morse, C., Wang, T., Chen, W., & Lafyatis, R. (2017) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2017.09.045 · Cited by 345
Valenzi, E., Bulik, M., Tabib, T., Morse, C., Sembrat, J., Bittar, H. E. T., Rojas, M. M., & Lafyatis, R. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-214865 · Cited by 353
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Croset, V., Treiber, C. D., & Waddell, S. (2018) · eLife · doi:10.7554/elife.34550 · Cited by 299
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Sun, S., Babola, T. A., Pregernig, G., So, K. S., Nguyen, M. H., Su, S.-S. M., Palermo, A. T., Bergles, D. E., Burns, J. C., & Müller, U. (2018) · Cell · doi:10.1016/j.cell.2018.07.008 · Cited by 340
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Brummelman, J., Mazza, E. M. C., Alvisi, G., Colombo, F. S., Grilli, A., Mikulak, J., Mavilio, D., Alloisio, M., Ferrari, F., Lopci, E., et al. (2018) · Journal of Experimental Medicine · doi:10.1084/jem.20180684 · Cited by 334
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Zhong, L., Yao, L., Tower, R. J., Wei, Y., Miao, Z., Park, J., Shrestha, R., Wang, L., Yu, W. H., Holdreith, N., et al. (2020) · eLife · doi:10.7554/elife.54695 · Cited by 345
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Burl, R. B., Ramseyer, V. D., Rondini, E. A., Pique-Regi, R., Lee, Y.-H., & Granneman, J. G. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.05.025 · Cited by 332
Kalluri, A. S., Vellarikkal, S. K., Edelman, E. R., Nguyễn, L., Subramanian, A., Ellinor, P. T., Regev, A., Kathiresan, S., & Gupta, R. M. (2019) · Circulation · doi:10.1161/circulationaha.118.038362 · Cited by 335
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“After filtration, standard scRNA-seq analysis (differential expression, marker gene detection, and clustering) was performed using the Seurat package (version 2.3) [33], from a merged expression matrice of four samples.”
MethodInfluentialScaling by shrinking: empowering single-cell 'omics' with microfluidic devices
Prakadan, S. M., Shalek, A. K., & Weitz, D. A. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.15 · Cited by 335
Single-Cell RNA Sequencing Reveals a Dynamic Stromal Niche That Supports Tumor Growth
Davidson, S., Efremova, M., Riedel, A., Mahata, B., Pramanik, J., Huuhtanen, J., Kar, G., Vento‐Tormo, R., Hagai, T., Chen, X. X., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107628 · Cited by 330
“Using graph-based clustering (Satija et al., 2015) and known marker expression, numerous immune and stromal populations were identified (Figures 1B, 1C, S1D).”
MethodThe triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321
CRISPR activation and interference screens decode stimulation responses in primary human T cells
Schmidt, R. L. J., Steinhart, Z., Layeghi, M., Freimer, J. W., Bueno, R., Nguyen, V. Q., Blaeschke, F., Ye, C., & Marson, A. (2022) · Science · doi:10.1126/science.abj4008 · Cited by 316
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
Zappia, L., Phipson, B., & Oshlack, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006245 · Cited by 319
Sensory lesioning induces microglial synapse elimination via ADAM10 and fractalkine signaling
Gunner, G., Cheadle, L., Johnson, K. M., Ayata, P., Badimon, A., Mondo, E., Nagy, M. A., Liu, L., Bemiller, S. M., Kim, K.-W., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0419-y · Cited by 318
Root Regeneration Triggers an Embryo-like Sequence Guided by Hormonal Interactions
Efroni, I., Mello, A. J., Nawy, T., Ip, P.-L., Rahni, R., DelRose, N., Powers, A. N., Satija, R., & Birnbaum, K. D. (2016) · Cell · doi:10.1016/j.cell.2016.04.046 · Cited by 315
Self-citationSingh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308
Andrews, T. S., Atif, J., Liu, J. C., Perciani, C. T., Ma, X., Thoeni, C., Slyper, M., Eraslan, G., Segerstolpe, A., Manuel, J., et al. (2022) · Hepatology Communications · doi:10.1002/hep4.1854 · Cited by 307
Self-citationMitochondria metabolism sets the species-specific tempo of neuronal development
Iwata, R., Casimir, P., Erkol, E., Boubakar, L., Planque, M., Gallego López, I. M., Ditkowska, M., Gaspariunaite, V., Beckers, S., Remans, D., et al. (2023) · Science · doi:10.1126/science.abn4705 · Cited by 312
Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis
Rohlenová, K., Goveia, J., García‐Caballero, M., Subramanian, A., Kalucka, J. M., Treps, L., Falkenberg, K. D., de Rooij, L. P. M. H., Zheng, Y., Lin, L., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.03.009 · Cited by 317
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
Duò, A., Robinson, M. D., & Soneson, C. (2018) · F1000Research · doi:10.12688/f1000research.15666.2 · Cited by 296
Associating cellular epigenetic models with human phenotypes
Lappalainen, T., & Greally, J. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.32 · Cited by 282
pcaReduce: hierarchical clustering of single cell transcriptional profiles
Žurauskienė, J., & Yau, C. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0984-y · Cited by 302
Davis, R. T., Blake, K., Ma, D., Gabra, M. B. I., Hernandez, G. A., Phung, A. T., Yang, Y., Maurer, D., Lefebvre, A. E. Y. T., Alshetaiwi, H. S., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0477-0 · Cited by 301
Khan, N., Downey, J., Sanz, J., Kaufmann, E., Blankenhaus, B., Pacis, A., Pernet, E., Ahmed, E. A., Cardoso, S., Nijnik, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.062 · Cited by 306
“Data integration across conditions Data were integrated across conditions using the anchoring routine proposed in Seurat (FindIntegrationAnchors) (Satija et al., 2015; Stuart et al., 2019), applied to the sub-matrix constituted by the highly informative genes previously identified.”
Arisdakessian, C. G., Poirion, O., Yunits, B., Zhu, X., & Garmire, L. X. (2019) · Genome biology · doi:10.1186/s13059-019-1837-6 · Cited by 302
Liu, Y., He, S., Wang, X., Peng, W., Chen, Q., Chi, D., Chen, J., Han, B., Lin, G., Li, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21043-4 · Cited by 297
M3Drop: dropout-based feature selection for scRNASeq
Andrews, T. S., & Hemberg, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1044 · Cited by 298
Molecular atlas of the adult mouse brain
Ortiz, C., Navarro, J. F., Jurek, A., Märtin, A., Lundeberg, J., & Meletis, K. (2020) · Science Advances · doi:10.1126/sciadv.abb3446 · Cited by 286
Wang, P., Zhang, J., Sun, L., Ma, Y., Xu, J., Liang, S., Deng, J., Tan, J., Zhang, Q., Tu, L., et al. (2017) · Plant Biotechnology Journal · doi:10.1111/pbi.12755 · Cited by 296
Chen, Y., Yang, S., Tavormina, J., Tampe, D., Zeisberg, M., Wang, H., Mahadevan, K. K., Wu, C.-J., Sugimoto, H., Chang, C.-C., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.011 · Cited by 289
Fodoulian, L., Tuberosa, J., Rossier, D., Boillat, M., Kan, C., Pauli, V., Egervari, K., Lobrinus, J. A., Landis, B. N., Carleton, A., et al. (2020) · doi:10.1101/2020.03.31.013268 · Cited by 286
“The data was analyzed in R version 3.5.0 using the Seurat (Butler et al., 2018; Satija et al., 2015; Stuart et al., 2019) R package version 3.1.4 following the indications described in Durante et al. (Durante et al., 2020) and using custom scripts.”
MethodInfluentialA Brief Overview of Global Trends in MSC-Based Cell Therapy
Jovic, D., Yu, Y., Wang, D., Wang, K., Li, H., Xu, F., Liu, C., Liu, J., & Luo, Y. (2022) · Stem Cell Reviews and Reports · doi:10.1007/s12015-022-10369-1 · Cited by 305
“With the development of computational tools, it becomes possible to reduce the complexity, and cluster the cells based on their genome-wide gene expression similarity [88].”
MethodRavindra, N. G., Alfajaro, M. M., Gasque, V., Huston, N. C., Wan, H., Szigeti-Buck, K., Yasumoto, Y., Greaney, A. M., Habet, V., Chow, R. D., et al. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001143 · Cited by 258
“We used the standard scRNA-seq analysis pipeline for clustering [60].”
MethodAcquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA
Paulson, K. G., Voillet, V., McAfee, M. S., Hunter, D. S., Wagener, F. D., Perdicchio, M., Valente, W. J., Koelle, S., Church, C. D., Vandeven, N. A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06300-3 · Cited by 293
Nitzan, M., Karaiskos, N., Friedman, N., & Rajewsky, N. (2019) · Nature · doi:10.1038/s41586-019-1773-3 · Cited by 275
Predicting cell-to-cell communication networks using NATMI
Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A., & Forrest, A. R. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18873-z · Cited by 285
Dura, B., Choi, J.-Y., Zhang, K., Damsky, W., Thakral, D., Bosenberg, M., Craft, J. E., & Fan, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1173 · Cited by 285
A Cell Atlas of the Adult Drosophila Midgut
Hung, R., Hu, Y., Kirchner, R., Liu, Y., Xu, C., Comjean, A., Tattikota, S. G., Li, F., Song, W., Sui, S. H., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916820117 · Cited by 281
Neutrophil extracellular traps target senescent vasculature for tissue remodeling in retinopathy
Binet, F., Cagnone, G., Crespo-Garcia, S., Hata, M., Neault, M., Dejda, A., Wilson, A. M., Buscarlet, M., Mawambo, G. T., Howard, J. P., et al. (2020) · Science · doi:10.1126/science.aay5356 · Cited by 283
Single-cell sequencing techniques from individual to multiomics analyses
Kashima, Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., & Suzuki, A. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00499-2 · Cited by 270
Machine learning and statistical methods for clustering single-cell RNA-sequencing data
Petegrosso, R., Li, Z., & Kuang, R. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz063 · Cited by 284
Complement Signals Determine Opposite Effects of B Cells in Chemotherapy-Induced Immunity
Lu, Y., Zhao, Q., Liao, J., Song, E., Xia, Q., Pan, J., Li, Y., Li, J., Zhou, B., Ye, Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.015 · Cited by 281
scVAE: variational auto-encoders for single-cell gene expression data
Grønbech, C. H., Vording, M. F., Timshel, P. N., Sønderby, C. K., Pers, T. H., & Winther, O. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa293 · Cited by 277
Zeng, A., Li, H., Guo, L., Gao, X., McKinney, S., Wang, Y., Yu, Z., Park, J., Semerad, C. L., Ross, E. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.006 · Cited by 280
Liao, J., Lu, X., Shao, X., Zhu, L., & Fan, X. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.05.006 · Cited by 282
Single‐Cell Analysis Using Droplet Microfluidics
Matuła, K., Rivello, F., & Huck, W. T. S. (2019) · Advanced Biosystems · doi:10.1002/adbi.201900188 · Cited by 284
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250
A single-cell transcriptome atlas for zebrafish development
Farnsworth, D. R., Saunders, L. M., & Miller, A. C. (2019) · Developmental Biology · doi:10.1016/j.ydbio.2019.11.008 · Cited by 272
Shami, A. N., Zheng, X., Munyoki, S. K., Ma, Q., Manske, G. L., Green, C. D., Sukhwani, M., Orwig, K. E., Li, J. Z., & Hammoud, S. S. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.05.010 · Cited by 283
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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