The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
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
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 701–800 of 1,006
Cell identity specification in plants: lessons from flower development
Xu, X., Smaczniak, C., Muiño, J. M., & Kaufmann, K. (2021) · Journal of Experimental Botany · doi:10.1093/jxb/erab110 · Cited by 32
Single cell RNA sequencing approaches to cardiac development and congenital heart disease
Samad, T., & Wu, S. M. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.04.023 · Cited by 33
Synthetic developmental biology: New tools to deconstruct and rebuild developmental systems
McNamara, H. M., Ramm, B., & Toettcher, J. E. (2022) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2022.04.013 · Cited by 33
Microfluidics Facilitates the Development of Single-Cell RNA Sequencing
Pan, Y., Cao, W., Mu, Y., & Zhu, Q. (2022) · Biosensors · doi:10.3390/bios12070450 · Cited by 32
Spacemake: processing and analysis of large-scale spatial transcriptomics data
Sztanka-Toth, T. R., Jens, M., Karaiskos, N., & Rajewsky, N. (2022) · GigaScience · doi:10.1093/gigascience/giac064 · Cited by 32
The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment
Zhao, J., Shi, Y., & Cao, G. (2023) · Biomolecules · doi:10.3390/biom13020344 · Cited by 32
Spatial transcriptomics: recent developments and insights in respiratory research
Wang, W., Chu, L., He, L., Zhang, M., Dang, K., Gao, C., Ge, Q., Wang, Z., & Zhao, X. (2023) · Military Medical Research · doi:10.1186/s40779-023-00471-x · Cited by 33
Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization
McGinnis, L. M., Ibarra-Lopez, V., Rost, S., & Ziai, J. M. (2021) · The Journal of Pathology · doi:10.1002/path.5663 · Cited by 32
A Molecular Voyage: Multiomics Insights into Circulating Tumor Cells
Zhang, Y. W., Gvozdenovic, A., & Aceto, N. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0218 · Cited by 31
Applications for single-cell and spatial transcriptomics in plant research
Sang, Q., & Kong, F. (2024) · New Crops · doi:10.1016/j.ncrops.2024.100025 · Cited by 31
Lv, X., Wang, B., Liu, K., Li, M. J., Yi, X., & Wu, X. (2024) · iScience · doi:10.1016/j.isci.2024.110064 · Cited by 31
Spatial omics enters the microscopic realm: opportunities and challenges
Si, Y., Lee, J. S., Jun, G., Kang, H. M., & Lee, J. H. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.05.002 · Cited by 31
Xing, Q. R., Cipta, N. O., Hamashima, K., Liou, Y., Koh, C.-G., & Loh, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00662 · Cited by 31
Visualizing T-Cell Responses: The T-Cell PET Imaging Toolbox
Li, C., Han, C., Duan, S., Li, P., Alam, I. S., & Xiao, Z. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.261976 · Cited by 31
Perspectives on rigor and reproducibility in single cell genomics
Gibson, G. C. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010210 · Cited by 31
Identification of cell-type-specific spatially variable genes accounting for excess zeros
Yu, J., & Luo, X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac457 · Cited by 31
SpaceX: gene co-expression network estimation for spatial transcriptomics
Acharyya, S., Zhou, X., & Baladandayuthapani, V. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac645 · Cited by 31
Preimplantation development in ungulates: a ‘ménage à quatre’ scenario
Artus, J., Hue, I., & Acloque, H. (2019) · Reproduction · doi:10.1530/rep-19-0348 · Cited by 31
Li, K., Ouyang, Z., Chen, Y., Gagnon, J., Lin, D., Mingueneau, M., Chen, W., Sexton, D., & Zhang, B. (2020) · Research Square · doi:10.1101/2020.08.28.270652 · Cited by 31
A Single-Cell Tumor Immune Atlas for Precision OncologyPreprint
Nieto, P., Elosua-Bayés, M., Trincado, J. L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A. R., Mereu, E., Moutinho, C., Ruiz, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.26.354829 · Cited by 31
ScType enables fast and accurate cell type identification from spatial transcriptomics data
Nader, K. M., Tascı, M., Ianevski, A., Erickson, A. M., Verschuren, E. W., Aittokallio, T. A., & Miihkinen, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae426 · Cited by 30
Yu, Z., Yang, Y., Chen, X., Wong, K., Zhang, Z., Zhao, Y., & Li, X. (2024) · Advanced Science · doi:10.1002/advs.202410081 · Cited by 30
Shao, Y., Lv, X.-Y., Ying, S., & Guo, Q. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2912404 · Cited by 30
SpaIM: single-cell spatial transcriptomics imputation via style transfer
Li, B., Tang, Z., Budhkar, A., Liu, X., Zhang, T., Yang, B., Su, J., & Song, Q. (2025) · Nature Communications · doi:10.1038/s41467-025-63185-9 · Cited by 29
Recent advances in tools to map the microbiome
Shi, H., Grodner, B., & Vlaminck, I. D. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100289 · Cited by 30
Machine learning methods to model multicellular complexity and tissue specificity
Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30
Single-Cell RNA Sequencing: Unravelling the Bone One Cell at a Time
Chai, R. C. C. (2022) · Current Osteoporosis Reports · doi:10.1007/s11914-022-00735-w · Cited by 30
Matsunaga, H., Arikawa, K., Yamazaki, M., Wagatsuma, R., Ide, K., Samuel, A. Z., Takamochi, K., Suzuki, K., Hayashi, T., Hosokawa, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-23651-6 · Cited by 30
Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics
Cao, J., Chen, X., Huang, S., Shi, W., Fan, Q., Gong, Y., Peng, Y., Wu, L., & Yang, C. J. (2022) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2022.116868 · Cited by 30
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
Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29
Johnson, J., Tsang, A. P., Mitchell, J. T., Zhou, D. L., Bowden, J., Davis-Marcisak, E. F., Sherman, T. D., Liefeld, T., Loth, M., Goff, L. A., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00892-x · Cited by 29
Wang, T., Zhu, H., Zhou, Y., Ding, W., Ding, W., Han, L., & Zhang, X. (2024) · Communications Biology · doi:10.1038/s42003-024-07037-0 · Cited by 29
Multimodal spatial transcriptomic characterization of mouse kidney injury and repair
Xuanyuan, Q., Wu, H., Sundaramoorthi, H., Isnard, P., Chen, C., Rahmani, W. M., & Humphreys, B. D. (2025) · Nature Communications · doi:10.1038/s41467-025-62599-9 · Cited by 30
A computational framework for DNA sequencing microscopy
Hoffecker, I. T., Yang, Y., Bernardinelli, G., Orponen, P., & Högberg, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821178116 · Cited by 29
Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology.
Teves, J. M., & Won, K. (2020) · PubMed Central · doi:10.14348/molcells.2020.0020 · Cited by 29
PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids
Jourdon, A., Scuderi, S., Capauto, D., Abyzov, A. S., & Vaccarino, F. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0763-3 · Cited by 29
Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants
Gurazada, S. G. R., Cox, K. L., Czymmek, K. J., & Meyers, B. C. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20200274 · Cited by 29
External signals regulate continuous transcriptional states in hematopoietic stem cells
Fast, E. M., Sporrij, A., Manning, M. E., da Rocha, E. L., Yang, S. P., Zhou, Y., Guo, J., Baryawno, N., Barkas, N., Scadden, D. T., et al. (2021) · eLife · doi:10.7554/elife.66512 · Cited by 30
Bowes, A. L., Tarabichi, M., Pillay, N., & Loo, P. V. (2022) · The Journal of Pathology · doi:10.1002/path.5914 · Cited by 29
Spatially resolved transcriptomics and the kidney: many opportunities
Dixon, E. E., Wu, H., Sulvarán-Guel, E., Guo, J., & Humphreys, B. D. (2022) · Kidney International · doi:10.1016/j.kint.2022.06.011 · Cited by 29
The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy
Lee, J. Y., Kannan, B., Lim, B. Y., Li, Z., Lim, A. H., Loh, J. W., Ko, T. K., Ng, C. C. Y., & Chan, J. Y. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147839 · Cited by 29
Pooled genetic screens with image‐based profiling
Walton, R. T., Singh, A. K., & Blainey, P. C. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110768 · Cited by 29
Ren, L., Huang, D., Liu, H., Lin, N., Cai, P., Yu, X., Zhang, Y., Luo, N., Lin, H., Su, J., et al. (2024) · Oncology Letters · doi:10.3892/ol.2024.14285 · Cited by 28
Single-cell colocalization analysis using a deep generative model
Kojima, Y., Mii, S., Hayashi, S., Hirose, H., Ishikawa, M., Akiyama, M., Enomoto, A., & Shimamura, T. (2024) · Cell Systems · doi:10.1016/j.cels.2024.01.007 · Cited by 29
Development and application of transcriptomics technologies in plant science
Wang, H., Xu, Y., Zhang, Z., Zhang, G., Tan, C., & Ye, L. (2024) · Crop Design · doi:10.1016/j.cropd.2024.100057 · Cited by 28
Cooper, R. A., Thomas, E. S., Sozanska, A. M., Pescia, C., & Royston, D. J. (2024) · Leukemia · doi:10.1038/s41375-024-02480-8 · Cited by 28
Spatial Transcriptomics in Human Cardiac Tissue
Nguyen, Q., Tung, L. W., Lin, B., Sivakumar, R., Sar, F., Singhera, G. K., Wang, Y., Parker, J., Bihan, S. L., Singh, A., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26030995 · Cited by 28
Chen, G. M., Azzam, A., Ding, Y., Barrett, D. M., Grupp, S. A., & Tan, K. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3888 · Cited by 28
Single cell analyses to understand the immune continuum in atherosclerosis
Hill, C. A., Fernandez, D. M., & Giannarelli, C. (2021) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2021.04.003 · Cited by 28
Molecular tracking devices quantify antigen distribution and archiving in the murine lymph node
Walsh, S. M., Sheridan, R. M., Lucas, E. D., Doan, T. A., Ware, B. C., Schafer, J., Fu, R., Burchill, M. A., Hesselberth, J. R., & Tamburini, B. A. J. (2021) · eLife · doi:10.7554/elife.62781 · Cited by 28
Integrative analysis of the human brain mural cell transcriptome
Gastfriend, B. D., Foreman, K. L., Katt, M. E., Palecek, S. P., & Shusta, E. V. (2021) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x211013700 · Cited by 28
Advancing root developmental research through single-cell technologies
Minne, M., Ke, Y., Saura-Sánchez, M., & Rybel, B. D. (2021) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2021.102113 · Cited by 28
Ospina, O. E., Wilson, C. M., Soupir, A. C., Berglund, A., Smalley, I., Tsai, K. Y., & Fridley, B. L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac145 · Cited by 28
Dissecting the Heterogeneity of Human Thoracic Aortic Aneurysms Using Single-Cell Transcriptomics
Mizrak, D., Feng, H., & Yang, B. (2022) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.317484 · Cited by 28
Marmolejo-Garza, A., Medeiros-Furquim, T., Rao, R. G., Eggen, B. J. L., Boddeke, E., & Dolga, A. M. (2022) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2022.119326 · Cited by 29
Diverse stem cells for periodontal tissue formation and regeneration
Nagata, M., English, J. D., Ono, N., & Ono, W. (2022) · genesis · doi:10.1002/dvg.23495 · Cited by 28
TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics
Shan, Y., Zhang, Q., Guo, W., Wu, Y., Miao, Y., Xin, H., Lian, Q., & Gu, J. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.012 · Cited by 28
Kong, S., Li, R., Tian, Y., Zhang, Y., Lu, Y., Ou, Q., Gao, P., Li, K., & Zhang, Y. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1100016 · Cited by 28
Lung development and regeneration: newly defined cell types and progenitor status
Meng, X., Cui, G., & Peng, G. (2023) · Cell Regeneration · doi:10.1186/s13619-022-00149-0 · Cited by 28
The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI
Lee, R. Y., Ng, C. W., Rajapakse, M. P., Ang, N., Yeong, J. P.-S., & Lau, M. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1172314 · Cited by 28
SOAR elucidates biological insights and empowers drug discovery through spatial transcriptomics
Li, Y., Ding, Y., Dennis, S., Hutch, M. R., Zhou, J., Zhou, Y., Li, Y., Pillai, M., Ghotbaldini, S., Garcia, M., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt7450 · Cited by 28
In Situ Transcriptome Accessibility Sequencing (INSTA-seq)Preprint
Fürth, D., Hatini, V., & Lee, J. H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/722819 · Cited by 28
Auerbach, B. J., Hu, J., Reilly, M. P., & Li, M. (2021) · Genome Research · doi:10.1101/gr.275430.121 · Cited by 28
Ma, C., Chitra, U., Zhang, S., & Raphael, B. J. (2022) · Cell Systems · doi:10.1016/j.cels.2022.09.002 · Cited by 27
Recent advances in spatially variable gene detection in spatial transcriptomics
Adhikari, S. D., Yang, J., Wang, J., & Cui, Y. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.01.016 · Cited by 27
scTrends: A living review of commercial single-cell and spatial 'omic technologies
Jonghe, J. D., Opzoomer, J. W., Vilas‐Zornoza, A., Nilges, B. S., Crane, P., Vicari, M., Lee, H., Lara‐Astiaso, D., Groß, T., Morf, J., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100723 · Cited by 27
Wang, R., Hastings, W. J., Saliba, J. G., Bao, D., Huang, Y., Maity, S., Ahmad, O. M. K., Hu, L., Wang, S., Fan, J., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c11505 · Cited by 27
Yoon, S. J., Lee, C. B., Chae, S. U., Jo, S. J., & Bae, S. K. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22136932 · Cited by 27
3D imaging for driving cancer discovery
van Ineveld, R. L., van Vliet, E. J., Wehrens, E. J., Alieva, M., & Rios, A. C. (2022) · The EMBO Journal · doi:10.15252/embj.2021109675 · Cited by 27
Deciphering Organoids: High-Dimensional Analysis of Biomimetic Cultures
Qin, X., & Tape, C. J. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.10.013 · Cited by 27
Spatial transcriptome sequencing of FFPE tissues at cellular level
Liu, Y., Enninful, A., Deng, Y., & Fan, R. (2020) · doi:10.1101/2020.10.13.338475 · Cited by 26
Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing
Yuan, C. U., Quah, F. X., & Hemberg, M. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101255 · Cited by 30
METI: deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics
Jiang, J., Liu, Y., Qin, J., Chen, J., Wu, J., Pizzi, M. P., Lazcano, R., Yamashita, K., Xu, Z., Pei, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51708-9 · Cited by 26
Temporal and spatial omics technologies for 4D profiling
Reynolds, D. E., Roh, Y. H., Oh, D., Vallapureddy, P., Fan, R., & Ko, J. (2025) · Nature Methods · doi:10.1038/s41592-025-02683-6 · Cited by 26
Niu, J., Fang, D., Chen, J., Xiong, Y., Liu, J., & Min, W. (2025) · Advanced Science · doi:10.1002/advs.202509090 · Cited by 25
Pyramidal neurons proportionately alter cortical interneuron subtypes
Wu, S. J., Dai, M., Yang, S.-P., McCann, C., Qiu, Y., Kumar, V., Marrero, G. J., Tsyporin, J., Huang, S., Shin, D., et al. (2026) · Nature · doi:10.1038/s41586-025-09996-8 · Cited by 26
Self-citationTaming human brain organoids one cell at a time
Atamian, A., Cordón-Barris, L., & Quadrato, G. (2020) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2020.05.022 · Cited by 26
Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data
Lu, T., Park, S., Zhu, J. J., Wang, Y., Zhan, X., Wang, X., Wang, L., Zhu, H., & Wang, T. (2021) · Cell Reports · doi:10.1016/j.celrep.2020.108589 · Cited by 26
Cang, Z., Wang, Y., Wang, Q., Cho, K. W. Y., Holmes, W. R., & Nie, Q. H. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008571 · Cited by 26
Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue
Noel, T., Wang, Q. S., Greka, A., & Marshall, J. L. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.809346 · Cited by 26
Integration of Computational Analysis and Spatial Transcriptomics in Single-Cell Studies
Wang, R., Peng, G., Tam, P., & Jing, N. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.06.006 · Cited by 26
Revealing the heterogeneity of CD4+ T cells through single-cell transcriptomics
Morgan, D. M., Shreffler, W. G., & Love, J. C. (2022) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2022.08.010 · Cited by 26
Integration tools for scRNA-seq data and spatial transcriptomics sequencing data
Yan, C., Zhu, Y., Chen, M., Yang, K., Cui, F., Zou, Q., & Zhang, Z. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae002 · Cited by 26
Tirumalasetty, M. B., Bhattacharya, I., Mohiuddin, M. S., Baki, V. B., & Choubey, M. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1394812 · Cited by 26
Parenting — a paradigm for investigating the neural circuit basis of behavior
Kohl, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.11.011 · Cited by 26
3D-cardiomics: A spatial transcriptional atlas of the mammalian heart
Mohenska, M., Tan, N. M., Tokolyi, A., Furtado, M. B., Costa, M. W., Perry, A., Hatwell-Humble, J., van Duijvenboden, K., Nim, H. T., Ji, Y. M. M., et al. (2021) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2021.09.011 · Cited by 26
Dai, Y., Teng, X., & Li, J. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202111132 · Cited by 26
Hu, J., Li, X., Coleman, K., Schroeder, A. R., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.30.405118 · Cited by 26
Belchikov, N., Hsu, J., Li, X. J., Jarroux, J., Hu, W., Joglekar, A., & Tilgner, H. (2024) · Genome Research · doi:10.1101/gr.279640.124 · Cited by 26
Purkinje Cell Patterning—Insights from Single-Cell Sequencing
Apsley, E. J., & Becker, E. B. E. (2022) · Cells · doi:10.3390/cells11182918 · Cited by 25
Wang, Y., Liu, Z., & Ma, X. (2024) · Genome biology · doi:10.1186/s13059-024-03272-0 · Cited by 30
Targeting secretory autophagy in solid cancers: mechanisms, immune regulation and clinical insights
Li, X., & Zhao, H. (2025) · Experimental Hematology and Oncology · doi:10.1186/s40164-025-00603-0 · Cited by 25
Unified molecular approach for spatial epigenome, transcriptome, and cell lineages
Huang, Y., Belk, J. A., Zhang, R., Weiser, N. E., Chiang, Z., Jones, M. G., Mischel, P. S., Buenrostro, J. D., & Chang, H. Y. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2424070122 · Cited by 25
Sonpatki, P., Park, H. S., Xing, Y. L., Han, K. Y., Schroeder, B., Yu, H. J., Kim, H. J., Chowdhury, T., Hwang, J. H., Nam, S. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69716-2 · Cited by 25
In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining
Woloshuk, A., Khochare, S., Almulhim, A., McNutt, A. T., Dean, D. F., Barwinska, D., Ferkowicz, M. J., Eadon, M. T., Kelly, K. J., Dunn, K. W., et al. (2020) · Cytometry Part A · doi:10.1002/cyto.a.24274 · Cited by 25
Smooth muscle cells in atherosclerosis: Clones but not carbon copies
Espinosa‐Díez, C., Mandi, V., Du, M., Liu, M., & Gomez, D. A. (2021) · JVS Vascular Science · doi:10.1016/j.jvssci.2021.02.002 · Cited by 25
Application of spatial transcriptome technologies to neurological diseases
Ya, D., Zhang, Y., Cui, Q., Jiang, Y., Yang, J., Tian, N., Xiang, W., Lin, X., Li, Q., & Liao, R. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1142923 · Cited by 25
Dissecting mammalian reproduction with spatial transcriptomics
Zhang, X., Cao, Q., Rajachandran, S., Grow, E. J., Evans, M., & Chen, H. (2023) · Human Reproduction Update · doi:10.1093/humupd/dmad017 · Cited by 25
Tippani, M., Divecha, H. R., Catallini, J. L., II, Kwon, S. H., Weber, L. M., Spangler, A., Jaffe, A. E., Hyde, T. M., Kleinman, J. E., Hicks, S. C., et al. (2023) · Biological Imaging · doi:10.1017/s2633903x23000235 · Cited by 25
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
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
