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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Open accessPublished version of a preprint
Abstract
Gene expression at fine scale: Mapping gene expression at the single-cell level within tissues remains a technical challenge. Rodriques et al. developed a method called Slide-seq, whereby RNA was spatially resolved from tissue sections by transfer onto a surface covered with DNA-barcoded beads. Applying Slide-seq to regions of a mouse brain revealed spatial gene expression patterns in the Purkinje layer of the cerebellum and axes of variation across Purkinje cell compartments. The authors used this method to dissect the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. Science , this issue p. 1463
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“Spatial profiling provides further information on tissue organization and three-dimensional architecture, thereby being essential for model validation186,187”
From Human disease models in drug development (2023) · cites this paper as [187]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Slide-seq captures transcripts on a slide equipped with random 10-µm DNA-barcoded beads and gains the positions of barcodes via in situ indexing, enabling spatial transcriptomic analysis at a near-cellular resolution [79].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [79]
In the citing paper
“While the technologies used here do not require harsh dissociation procedures like single-cell or single-nuclei approaches and maintain spatial information, the lack of single-cell resolution with ST and the low sensitivity of ISS requires further technological improvements (Eng et al. , 2019; Rodriques et al. , 2019; Vickovic et al. , 2019; Stickels et al. , 2020).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Rodriques et al., 2019
In the citing paper
“PET imaging of collagen and molecular imaging of fibrosis185,186, which allows fast, non-invasive, and whole-organ-quantitative and longitudinal readouts of drug efficacy in antifibrotic clinical trials”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
In the citing paper
“combining molecular imaging of fibrosis185 with cutting edge omic approaches such as single cell genomics174–176 could markedly improve patient diagnostics, staging, prognostication, stratification and cohort enrichment, which would in turn optimize clinical trial design”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
In the citing paper
“Spatially resolved sequencing uses slides pre-patterned with beads carrying unique DNA barcodes to tag mRNA molecules based on their location in a brain slice, enabling whole-transcriptome sequencing with ~10 μm spatial resolution (Rodriques et al., 2019; Ståhl et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
In the citing paper
“For Slide-seq (Rodriques et al., 2019), this is limited by spatial resolution (~10 μm)”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
In the citing paper
“Slide-seq, and later Slide-seqV2, barcode mRNA in tissues using a method conceptually similar to spatial transcriptomics, but a different strategy: instead of using arrayed barcoded reagents printed on slides, the barcoded reagents are placed directly onto the glass slide in solution, thereby creating a monolayer-packed array on a glass coverslip118,119 (Fig. 4a).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [118]
In the citing paper
“Nonetheless, we did observe the previously reported aging-related decline of OPC 49 , NRP 35,36 and ImmN 36,50 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [50]
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Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Self-citationVisualization 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
A pyramid approach to subpixel registration based on intensity
Thévenaz, P., Ruttimann, U. E., & Unser, M. A. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.650848 · Cited by 3,097
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
Conditional gene targeting in macrophages and granulocytes using LysMcre mice
Clausen, B. E., Burkhardt, C., Reith, W., Renkawitz, R., & Förster, I. (1999) · Transgenic Research · doi:10.1023/a:1008942828960 · Cited by 2,098
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,531
Self-citationStoodley, C. J., & Schmahmann, J. D. (2010) · Cortex · doi:10.1016/j.cortex.2009.11.008 · Cited by 1,360
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Items 501–600 of 1,006
Heezen, L. G. M., Abdelaal, T., van Putten, M., Aartsma-Rus, A. M., Mahfouz, A. E., & Spitali, P. (2023) · Nature Communications · doi:10.1038/s41467-023-40555-9 · Cited by 54
A spatiotemporal molecular atlas of the ovulating mouse ovary
Mantri, M., Zhang, H., Spanos, E., Ren, Y. A., & Vlaminck, I. D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317418121 · Cited by 57
STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks
Li, Y., & Luo, Y. (2024) · Genome biology · doi:10.1186/s13059-024-03353-0 · Cited by 54
Cross-species single-cell spatial transcriptomic atlases of the cerebellar cortex
Hao, S., Zhu, X., Huang, Z. R., Yang, Q., Liu, H.-C., Wu, Y. H., Zhan, Y., Dong, Y., Li, C., Wang, H., et al. (2024) · Science · doi:10.1126/science.ado3927 · Cited by 54
Camps, J., Jiménez-Franco, A., García-Pablo, R., Joven, J., & Arenas, M. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167841 · Cited by 54
Applications of Single-Cell Omics in Tumor Immunology
Liu, J., Qu, S., Zhang, T., Gao, Y., Shi, H., Song, K., Chen, W., & Yin, W. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.697412 · Cited by 53
STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning
Zhang, C., Dong, K., Aihara, K., Chen, L., & Zhang, S. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad801 · Cited by 54
Self-citationNeoantigen cancer vaccines: a new star on the horizon
Li, X., You, J., Hong, L., Liu, W., Guo, P., & Hao, X. (2023) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2023.0395 · Cited by 53
Plasticity and heterogeneity of thermogenic adipose tissue
Sun, W., Modica, S., Dong, H., & Wolfrum, C. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00417-4 · Cited by 52
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Tang, M., Zhao, M., & Shi, Y. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1282658 · Cited by 52
Zhu, J., Pang, K., Hu, B., He, R., Wang, N., Jiang, Z., Ji, P., & Zhao, F. (2024) · Nature Genetics · doi:10.1038/s41588-024-01906-4 · Cited by 56
Yang, Y., Cui, Y., Zeng, X., Zhang, Y., Loza, M., Park, S., & Nakai, K. (2025) · Nature Communications · doi:10.1038/s41467-025-56276-0 · Cited by 55
Advances in spatial transcriptomics and its application in the musculoskeletal system
Wang, H., Cheng, P., Wang, J., Lv, H., Han, J., Hou, Z., Xu, R., & Chen, W. (2025) · Bone Research · doi:10.1038/s41413-025-00429-w · Cited by 53
Sequencing-free whole-genome spatial transcriptomics at single-molecule resolution
Cheng, Y., Dang, S., Zhang, Y., Chen, Y., Yu, R., Miao, L., Jin, S., Han, A., Katz, S. G., & Wang, S. (2025) · Cell · doi:10.1016/j.cell.2025.09.006 · Cited by 53
Kita, Y., Nishibe, H., Wang, Y., Hashikawa, T., Kikuchi, S. S., Mami, U., Yoshida, A., Yoshida, C., Kawase, T., Ishii, S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2020125118 · Cited by 52
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Cole, B., Bergmann, D. C., Blaby, C., Blaby, I. K., Bouchard, K. E., Brady, S. M., Ciobanu, D., Coleman‐Derr, D., Leiboff, S., Mortimer, J. C., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02477-4 · Cited by 52
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Ryu, K. H., Zhu, Y., & Schiefelbein, J. (2021) · Annual Review of Genetics · doi:10.1146/annurev-genet-071719-020453 · Cited by 53
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Schreibing, F., Anslinger, T. M., & Kramann, R. (2023) · Circulation Research · doi:10.1161/circresaha.122.321761 · Cited by 51
Hu, K. H., Kuhn, N. F., Courau, T., Tsui, J. L., Samad, B., Ha, P. K., Kratz, J. R., Combes, A. J., & Krummel, M. F. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.05.001 · Cited by 52
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431 · Cited by 55
Tracking cell-type-specific temporal dynamics in human and mouse brains
Lu, Z., Zhang, M., Lee, J., Sziraki, A., Anderson, S., Zhang, Z., Xu, Z., Jiang, W., Ge, S., Nelson, P. T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.042 · Cited by 51
MENDER: fast and scalable tissue structure identification in spatial omics data
Yuan, Z. (2024) · Nature Communications · doi:10.1038/s41467-023-44367-9 · Cited by 52
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Cervilla, S., Mendoza, D. G., Perez, E., Real, F. X., Musulén, E., Aprea, J., Esteller, M., & Porta‐Pardo, E. (2026) · Genome biology · doi:10.1186/s13059-026-03937-y · Cited by 51
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Shi, H., Yan, K., Ding, L., Qian, C., Chi, H., & Yu, J. (2020) · iScience · doi:10.1016/j.isci.2020.101354 · Cited by 51
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Carstens, J. L., Krishnan, S., Rao, A. U. K., Sorace, A. G., Seeley, E. H., Borgogno, S. F., & Burks, J. K. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00330-6 · Cited by 50
Altered oligodendroglia and astroglia in chronic traumatic encephalopathy
Chancellor, K. B., Chancellor, S. E., Duke-Cohan, J. E., Huber, B. R., Stein, T. D., Alvarez, V. E., Okaty, B. W., Dymecki, S. M., & McKee, A. C. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02322-2 · Cited by 51
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Shekhar, K., & Sanes, J. R. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-032621-075200 · Cited by 51
Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics
Lin, S., Liu, Y., Zhang, M., Xu, X. J., Chen, Y., Zhang, H., & Yang, C. J. (2021) · Lab on a Chip · doi:10.1039/d1lc00607j · Cited by 50
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Dorado, G., Gálvez, S., Tham, T. E. R., Sánchez, V. F. V., & Hernández, P. (2021) · Biomolecules · doi:10.3390/biom11081111 · Cited by 49
Liu, Z., Wu, D., Zhai, W., & Ma, L. (2023) · Nature Communications · doi:10.1038/s41467-023-40458-9 · Cited by 52
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Sinha, S., Satpathy, A. T., Zhou, W., Ji, H., Stratton, J. A., Jaffer, A., Bahlis, N. J., Morrissy, A. S., & Biernaskie, J. A. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.06.010 · Cited by 49
Deleersnijder, D., Callemeyn, J., Arijs, I., Naesens, M., Craenenbroeck, A. H. V., Lambrechts, D., & Sprangers, B. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021020157 · Cited by 49
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Zeng, H., Dong, H., & Zhuang, X. (2020) · doi:10.1101/2020.06.04.105700 · Cited by 44
Yan, G., Hua, S. H., & Li, J. J. (2025) · Nature Communications · doi:10.1038/s41467-025-56080-w · Cited by 48
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Palmer, J. A., Rosenthal, N. A., Teichmann, S. A., & Litviňuková, M. (2024) · Circulation Research · doi:10.1161/circresaha.124.323672 · Cited by 48
Cesaro, G., Nagai, J. S., Nicolò, G., Chiodi, A., Tussardi, G., Klöker, V., Musumarra, C. V., Mosca, E., Costa, I. G., Camillo, B. D., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf280 · Cited by 48
Starfish enterprise: finding RNA patterns in single cells
Perkel, J. M. (2019) · Nature · doi:10.1038/d41586-019-02477-9 · Cited by 48
Bae, S., Choi, H., & Lee, D. S. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab095 · Cited by 48
Single-cell transcriptomic analysis of mIHC images via antigen mapping
Govek, K. W., Troisi, E. C., Miao, Z., Aubin, R. G., Woodhouse, S., & Cámara, P. G. (2021) · Science Advances · doi:10.1126/sciadv.abc5464 · Cited by 48
Spatial Transcriptomics: Molecular Maps of the Mammalian Brain
Ortiz, C., Carlén, M., & Meletis, K. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100520-082639 · Cited by 48
Laureyns, R., Joossens, J., Herwegh, D., Pevernagie, J., Pavie, B., Demuynck, K., Debray, K., Coussens, G., Pauwels, L., Hautegem, T. V., et al. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab533 · Cited by 48
Bridges, K., & Miller‐Jensen, K. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.885267 · Cited by 48
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Kueckelhaus, J., von Ehr, J., Ravi, V. M., Will, P., Joseph, K., Beck, J., Hofmann, U., Delev, D., Schnell, O., & Heiland, D. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.20.346544 · Cited by 48
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
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Dumas, A. A., Borst, K., & Prinz, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.004 · Cited by 47
High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer
Koblan, L. W., Yost, K. E., Zheng, P., Colgan, W. N., Jones, M. G., Yang, D., Kumar, A., Sandhu, J., Schnell, A., Sun, D., et al. (2025) · Science · doi:10.1126/science.adx3800 · Cited by 53
Single-cell genomics to understand disease pathogenesis
Nomura, S. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00844-3 · Cited by 47
Oligodendroglia heterogeneity in the human central nervous system
Seeker, L. A., & Williams, A. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02390-4 · Cited by 47
Tissue mechanics coevolves with fibrillar matrisomes in healthy and fibrotic tissues
Dooling, L. J., Saini, K., Anlaş, A. A., & Discher, D. E. (2022) · Matrix Biology · doi:10.1016/j.matbio.2022.06.006 · Cited by 46
A single-cell rice atlas integrates multi-species data to reveal cis-regulatory evolution
Yan, H., Mendieta, J. P., Zhang, X., Luo, Z., Marand, A. P., Liang, Y. Q., Minow, M. A. A., Zhong, Y., Jin, Y., Jang, H., et al. (2025) · Nature Plants · doi:10.1038/s41477-025-02106-6 · Cited by 50
Moysés‐Oliveira, M., Yadav, R., Erdin, S., & Talkowski, M. E. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.07.001 · Cited by 46
Spatial tissue profiling by imaging-free molecular tomography
Schede, H. H., Schneider, C. G., Stergiadou, J., Borm, L. E., Ranjak, A., Yamawaki, T., David, F. P. A., Lönnerberg, P., Laurent, G., Tosches, M. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00879-7 · Cited by 46
Nagasawa, S., Kashima, Y., Suzuki, A., & Suzuki, Y. (2021) · Inflammation and Regeneration · doi:10.1186/s41232-021-00170-x · Cited by 46
Casingal, C. R., Descant, K. D., & Anton, E. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.034 · Cited by 46
Tumor vessel co-option: The past & the future
Cuypers, A., Truong, A.-C. K., Becker, L. M., Saavedra-García, P., & Carmeliet, P. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.965277 · Cited by 46
Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101536 · Cited by 45
Iqbal, F., Lupieri, A., Aikawa, M., & Aïkawa, E. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314776 · Cited by 45
Johnson, A., Laterra, J., & López-Bertoni, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.995498 · Cited by 45
Microtechnologies for single-cell and spatial multi-omics
Deng, Y., Bai, Z., & Fan, R. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00084-y · Cited by 45
Bafna, M., Li, H., & Zhang, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad269 · Cited by 45
Progress in single-cell multimodal sequencing and multi-omics data integration
Wang, X. J., Wu, X., Hong, N., & Jin, W. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01092-3 · Cited by 45
Opportunities and Challenges in Advancing Plant Research with Single-cell Omics
Rhaman, M. S., Ali, M., Ye, W., & Li, B. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae026 · Cited by 45
Spatial omics advances for in situ RNA biology
Ren, J., Luo, S., Shi, H., & Wang, X. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.08.002 · Cited by 45
Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics
Pentimalli, T. M., Karaiskos, N., & Rajewsky, N. (2024) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-111523-023417 · Cited by 45
Shi, W., Zhang, Z., Xu, X., Tian, Y., Feng, L., Huang, X., Du, Y., & Li, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1649468 · Cited by 45
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del Sol, A., & Jung, S. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.07.006 · Cited by 45
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Lisberger, S. G. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.08.026 · Cited by 45
Seq-Scope: Repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics
Kim, Y., Cheng, W., Cho, C.-S., Hwang, Y., Si, Y., Park, A., Schrank, M., Hsu, J.-E., Anacleto, A., Xi, J., et al. (2025) · Nature Protocols · doi:10.1038/s41596-024-01065-0 · Cited by 44
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
Genshaft, A. S., Ziegler, C. G. K., Tzouanas, C. N., Mead, B. E., Jaeger, A. M., Navia, A. W., King, R. P., Mana, M., Huang, S., Mitsialis, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25279-y · Cited by 44
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Lee, A. C., Lee, Y., Choi, A., Lee, H., Shin, K., Lee, H., Kim, J. Y., Ryu, H. S., Kim, H. S., Ryu, S. Y., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30299-3 · Cited by 44
Liang, Y., Shi, G., Cai, R., Yuan, Y., Xie, Z., Yu, L., Huang, Y., Shi, Q., Wang, L., Li, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44835-w · Cited by 49
Zhao, C., Xu, Z., Wang, X., Tao, S., MacDonald, W. A., He, K., Poholek, A. C., Chen, K., Huang, H., & Chen, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae052 · Cited by 44
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Li, H., Bao, S., Farzad, N., Qin, X., Fung, A. A., Zhang, D., Bai, Z., Tao, B., & Fan, R. (2025) · Nature Protocols · doi:10.1038/s41596-025-01145-9 · Cited by 44
Ouyang, P., Zhang, J., He, X., Yang, C., Zeng, D., & Xu, D. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1661545 · Cited by 44
Boisson, A., Noel, G. L., Saiselet, M., Vitória, J. R., Thomas, N., Fontsa, M. L., Sofronii, D., Naveaux, C., Duvillier, H., Craciun, L., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.673042 · Cited by 43
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Charytonowicz, D., Brody, R., & Sebra, R. (2023) · Nature Communications · doi:10.1038/s41467-023-36961-8 · Cited by 42
Shi, X., Zhu, J., Long, Y., & Liang, C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad278 · Cited by 42
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Kim, H., Kumar, A., Lövkvist, C., Palma, A. M., Martin, P. C. N., Kim, J., Bhoopathi, P., Trevino, J., Fisher, P. B., Madan, E., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311670 · Cited by 42
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Liu, X., Jiang, Y., Song, D., Zhang, L., Xu, G. S., Hou, R., Zhang, Y., Chen, J., Cheng, Y., Liu, L., et al. (2022) · Clinical and Translational Medicine · doi:10.1002/ctm2.669 · Cited by 42
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Maurer, K., Park, C. Y., Mani, S., Borji, M., Raths, F., Gouin, K. H., Penter, L., Jin, Y., Zhang, J. Y., Shin, C. S., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adr0782 · Cited by 42
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Metadata
- Authors
- 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.
- Affiliations
- Broad Institute (US)
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
- Corresponding author
- Chen, Fei
- Macosko, Evan Z.
- ORCID
- Venue
- Science
- Published
- 2019-03-29
- Volume
- 363
- Issue
- 6434
- Pages
- 1463-1467
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6927209
- OpenAlex
- W2915848182
- Cited by
- 2,424
- References
- 49
- Citations by year
- 2026: 385 · 2025: 391 · 2024: 415 · 2023: 319 · 2022: 300 · 2021: 318 · 2020: 212 · 2019: 80
- FWCI
- 75.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH Office of the Director (DP2 AG058488-01, 1DP5OD024583)
- 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 › Molecular Biology Techniques and Applications
- Keywords
- Slide-seq, spatial transcriptomics, cerebellum, hippocampus, traumatic brain injury, single-cell RNA sequencing, mouse model
- MeSH terms
- Brain Injuries, Traumatic, Animals, Cerebellum, Disease Models, Animal, Frozen Sections, Gene Expression Regulation, Hippocampus, Purkinje Cells, RNA, Messenger, Transcription, Genetic, Sequence Analysis, RNA, Cell Size, Mice, Genome-Wide Association Study, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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