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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Open accessAbstract
Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
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Cites
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
Trapnell, 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
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Genome-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
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493
TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,212
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,586
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Optimal Transport: Old and New
Villani, C. (2013) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-3-540-71050-9 · Cited by 3,838
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449
Central Dogma of Molecular Biology
CRICK, F. (1970) · Nature · doi:10.1038/227561a0 · Cited by 3,444
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
B cells and tertiary lymphoid structures promote immunotherapy response
Helmink, B. A., Reddy, S. M., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, M., Blando, J., Han, G., et al. (2020) · Nature · doi:10.1038/s41586-019-1922-8 · Cited by 2,731
Puram, S. V., Tirosh, I., Parikh, A. S., Patel, A. P., Yizhak, K., Gillespie, S., Rodman, C., Luo, C. L., Mroz, E. A., Emerick, K. S., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.044 · Cited by 2,557
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413
The tumor microenvironment and its role in promoting tumor growth
Whiteside, T. L. (2008) · Oncogene · doi:10.1038/onc.2008.271 · Cited by 2,376
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
NicheNet: Modeling intercellular communication by linking ligands to target genes
Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,866
The tumor microenvironment at a glance
Balkwill, F. R., Capasso, M., & Hagemann, T. (2012) · Journal of Cell Science · doi:10.1242/jcs.116392 · Cited by 1,857
Single-cell RNA sequencing to explore immune cell heterogeneity
Papalexi, E., & Satija, R. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.76 · Cited by 1,806
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH
Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754
Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
CellMarker: a manually curated resource of cell markers in human and mouse
Zhang, X., Lan, Y., Xu, J., Quan, F., Zhao, E., Deng, C., Luo, T., Xu, L., Liao, G., Yan, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky900 · Cited by 1,524
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508
Visualization of Single RNA Transcripts in Situ
Femino, A. M., Fay, F. S., Fogarty, K., & Singer, R. H. (1998) · Science · doi:10.1126/science.280.5363.585 · Cited by 1,504
Deep profiling of mouse splenic architecture with CODEX multiplexed imaging
Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & Nolan, G. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.010 · Cited by 1,501
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0739-1 · Cited by 1,295
Deciphering cell–cell interactions and communication from gene expression
Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267
Challenges in unsupervised clustering of single-cell RNA-seq data
Kiselev, V. Y., Andrews, T. S., & Hemberg, M. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0088-9 · Cited by 1,205
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193
Gulati, A., Jabbour, A., Ismail, T. F., Guha, K., Khwaja, J., Raza, S., Morarji, K., Brown, T. D. H., Ismail, N. A., Dweck, M. R., et al. (2013) · JAMA · doi:10.1001/jama.2013.1363 · Cited by 1,183
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170
High-definition spatial transcriptomics for in situ tissue profiling
Vickovic, S., Eraslan, G., Salmén, F., Klughammer, J., Stenbeck, L., Schapiro, D., Äijö, T., Bonneau, R., Bergenstråhle, L., Navarro, J. F., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0548-y · Cited by 1,150
Method of the Year: spatially resolved transcriptomics
Marx, V. (2021) · Nature Methods · doi:10.1038/s41592-020-01033-y · Cited by 1,132
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117
Self-citationA benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113
Multiplexed ion beam imaging of human breast tumors
Angelo, M., Bendall, S. C., Finck, R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. B., Liu, S. D., Zhao, S., et al. (2014) · Nature Medicine · doi:10.1038/nm.3488 · Cited by 1,108
Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations
van den Brink, S. C., Sage, F., Vértesy, Á., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., Robin, C., & van Oudenaarden, A. (2017) · Nature Methods · doi:10.1038/nmeth.4437 · Cited by 1,072
Multiplex digital spatial profiling of proteins and RNA in fixed tissue
Merritt, C. R., Ong, G. T., Church, S. E., Barker, K., Danaher, P., Geiss, G., Hoang, M., Jung, J., Liang, Y., McKay-Fleisch, J., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0472-9 · Cited by 1,072
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Single-cell spatial reconstruction reveals global division of labour in the mammalian liver
Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., et al. (2017) · Nature · doi:10.1038/nature21065 · Cited by 1,059
Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045
Single-cell in situ RNA profiling by sequential hybridization
Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M., & Cai, L. (2014) · Nature Methods · doi:10.1038/nmeth.2892 · Cited by 985
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 966
High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue
Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.026 · Cited by 966
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02286-2 · Cited by 966
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949
In situ sequencing for RNA analysis in preserved tissue and cells
Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934
Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
Wang, X., Park, J., Suszták, K., Zhang, N. R., & Li, M. (2019) · Nature Communications · doi:10.1038/s41467-018-08023-x · Cited by 926
Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela‐Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S. F. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0439-6 · Cited by 894
Spatial multi-omic map of human myocardial infarction
(2020) · doi:10.1101/2020.12.08.411686 · Cited by 772
Spatial multi-omic map of human myocardial infarction
Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., Liao, X., Hannani, M. T., Tanevski, J., Wünnemann, F., Nagai, J. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05060-x · Cited by 772
Xia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912459116 · Cited by 755
A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 754
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732
Naturally enhanced neutralizing breadth against SARS-CoV-2 one year after infection
Wang, Z., Muecksch, F., Schaefer-Babajew, D., Finkin, S., Viant, C., Gaebler, C., Hoffmann, H. H., Barnes, C. O., Cipolla, M., Ramos, V., et al. (2021) · Nature · doi:10.1038/s41586-021-03696-9 · Cited by 721
Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue
Gerdes, M. J., Sevinsky, C. J., Sood, A., Adak, S., Bello, M. O., Bordwell, A., Can, A. B., Corwin, A. D., Dinn, S. R., Filkins, R. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300136110 · Cited by 716
SpatialDE: identification of spatially variable genes
Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695
Immune Landscape of Viral- and Carcinogen-Driven Head and Neck Cancer
Cillo, A. R., Kürten, C. H. L., Tabib, T., Qi, Z., Onkar, S., Wang, T., Liu, A., Duvvuri, U., Kim, S., Soose, R. J., et al. (2020) · Immunity · doi:10.1016/j.immuni.2019.11.014 · Cited by 677
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671
Spatially resolved transcriptomes—next generation tools for tissue exploration
Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643
Inference and analysis of cell-cell communication using CellChatPreprint
Jin, S., Guerrero‐Juarez, C. F., Zhang, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.21.214387 · Cited by 604
Spatial proteomics: a powerful discovery tool for cell biology
Lundberg, E., & Borner, G. H. H. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0094-y · Cited by 591
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method
Lin, J., Fallahi‐Sichani, M., & Sorger, P. K. (2015) · Nature Communications · doi:10.1038/ncomms9390 · Cited by 562
Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin
Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B., & Zhuang, X. (2020) · Cell · doi:10.1016/j.cell.2020.07.032 · Cited by 562
Single-Cell Gene Expression Profiling
Levsky, J. M., Shenoy, S. M., Pezo, R. C., & Singer, R. H. (2002) · Science · doi:10.1126/science.1072241 · Cited by 526
Spatiotemporal analysis of human intestinal development at single-cell resolution
Fawkner-Corbett, D., Antanaviciute, A., Parikh, K., Jagielowicz, M., Gerós, A. S., Gupta, T., Ashley, N., Khamis, D., Fowler, D. J., Morrissey, E. R., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.016 · Cited by 521
Microscopic examination of spatial transcriptome using Seq-Scope
Cho, C.-S., Xi, J., Si, Y., Park, S.-R., Hsu, J.-E., Kim, M., Jun, G., Kang, H. M., & Lee, J. H. (2021) · Cell · doi:10.1016/j.cell.2021.05.010 · Cited by 520
Multiplexed protein maps link subcellular organization to cellular states
Gut, G., Herrmann, M. D., & Pelkmans, L. (2018) · Science · doi:10.1126/science.aar7042 · Cited by 510
Integrating spatial gene expression and breast tumour morphology via deep learning
He, B., Bergenstråhle, L., Stenbeck, L., Abid, A., Andersson, A., Borg, Å., Maaskola, J., Lundeberg, J., & Zou, J. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0578-x · Cited by 507
Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies
Sun, S., Zhu, J., & Zhou, X. (2020) · Nature Methods · doi:10.1038/s41592-019-0701-7 · Cited by 502
Moor, A. E., Harnik, Y., Ben-Moshe, S., Massasa, E. E., Rozenberg, M., Eilam, R., Bahar Halpern, K., & Itzkovitz, S. (2018) · Cell · doi:10.1016/j.cell.2018.08.063 · Cited by 480
Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography
Andersson, A., Bergenstråhle, J., Asp, M., Bergenstråhle, L., Jurek, A., Navarro, J. F., & Lundeberg, J. (2020) · Communications Biology · doi:10.1038/s42003-020-01247-y · Cited by 450
Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al. (2019) · Science · doi:10.1126/science.aav9776 · Cited by 429
Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems
Alon, S., Goodwin, D., Sinha, A., Wassie, A. T., Chen, F., Daugharthy, E. R., Bando, Y., Kajita, A., Xue, A. G., Marrett, K., et al. (2021) · Science · doi:10.1126/science.aax2656 · Cited by 421
SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics
Cabello‐Aguilar, S., Alame, M., Kon-Sun-Tack, F., Fau, C., Lacroix, M., & Colinge, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa183 · Cited by 405
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
Cang, Z., & Nie, Q. H. (2020) · Nature Communications · doi:10.1038/s41467-020-15968-5 · Cited by 400
High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395
Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level
Deng, Y., Bartosovic, M., Kukanja, P., Zhang, D., Liu, Y., Su, G., Enninful, A., Bai, Z., Castelo-Branco, G., & Fan, R. (2022) · Science · doi:10.1126/science.abg7216 · Cited by 393
A comprehensive transcriptional map of primate brain development
Bakken, T. E., Miller, J. A., Ding, S., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Dalley, R. A., Royall, J. J., Lemon, T. A., et al. (2016) · Nature · doi:10.1038/nature18637 · Cited by 387
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383
The Drosophila embryo at single-cell transcriptome resolution
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
Winkler, E. S., Gilchuk, P., Yu, J., Bailey, A. L., Chen, R. E., Chong, Z., Zost, S. J., Jang, H., Huang, Y., Allen, J. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.026 · Cited by 366
Microglia-Mediated Inflammation and Neurodegenerative Disease
Xu, L., Dan, H., & Bai, Y. (2015) · Molecular Neurobiology · doi:10.1007/s12035-015-9593-4 · Cited by 340
Thrane, K., Eriksson, H., Maaskola, J., Hansson, J., & Lundeberg, J. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0747 · Cited by 331
Genome-wide RNA Tomography in the Zebrafish Embryo
Junker, J. P., Noël, E. S., Guryev, V., Peterson, K. A., Shah, G., Huisken, J., McMahon, A. P., Berezikov, E., Bakkers, J., & van Oudenaarden, A. (2014) · Cell · doi:10.1016/j.cell.2014.09.038 · Cited by 314
Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer
Ali, H. R., Jackson, H. W., Zanotelli, V. R., Danenberg, E., Fischer, J. R., Bardwell, H. A., Provenzano, E., CRUK IMAXT Grand Challenge Team, Ali, H. R., Sa’d, M. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0026-6 · Cited by 313
A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry
Damond, N., Engler, S., Zanotelli, V. R., Schapiro, D., Wasserfall, C. H., Kusmartseva, I., Nick, H. S., Thorel, F., Herrera, P. L., Atkinson, M. A., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.11.014 · Cited by 301
SpatialDWLS: accurate deconvolution of spatial transcriptomic data
Dong, R., & Yuan, G. (2021) · Genome biology · doi:10.1186/s13059-021-02362-7 · Cited by 301
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
Gong, T., & Szustakowski, J. D. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt090 · Cited by 295
Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue
Lovatt, D., Ruble, B. K., Lee, J., Dueck, H., Kim, T. K., Fisher, S., Francis, C., Spaethling, J. M., Wolf, J. A., Grady, M. S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2804 · Cited by 287
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
Schulz, D., Zanotelli, V. R., Fischer, J. R., Schapiro, D., Engler, S., Lun, X., Jackson, H. W., & Bodenmiller, B. (2017) · Cell Systems · doi:10.1016/j.cels.2017.12.001 · Cited by 277
Dissecting cellular crosstalk by sequencing physically interacting cells
Giladi, A., Cohen, M., Medaglia, C., Baran, Y., Li, B., Zada, M., Bost, P., Blecher‐Gonen, R., Salame, T., Mayer, J. U., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0442-2 · Cited by 276
Saviano, A., Henderson, N. C., & Baumert, T. F. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.06.004 · Cited by 276
Nitzan, M., Karaiskos, N., Friedman, N., & Rajewsky, N. (2019) · Nature · doi:10.1038/s41586-019-1773-3 · Cited by 275
Monitoring T cell–dendritic cell interactions in vivo by intercellular enzymatic labelling
Pasqual, G., Chudnovskiy, A., Tas, J. M. J., Agudelo, M., Schweitzer, L. D., Cui, A., Hacohen, N., & Victora, G. D. (2018) · Nature · doi:10.1038/nature25442 · Cited by 270
Seamless integration of image and molecular analysis for spatial transcriptomics workflows
Bergenstråhle, J., Larsson, L., & Lundeberg, J. (2020) · BMC Genomics · doi:10.1186/s12864-020-06832-3 · Cited by 263
Quantum dot imaging platform for single-cell molecular profiling
Zrazhevskiy, P., & Gao, X. (2013) · Nature Communications · doi:10.1038/ncomms2635 · Cited by 253
SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz166 · Cited by 250
Identifying cell populations with scRNASeq
Andrews, T. S., & Hemberg, M. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.002 · Cited by 247
Computational deconvolution of transcriptomics data from mixed cell populations
Cobos, F. A., Vandesompele, J., Mestdagh, P., & Preter, K. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty019 · Cited by 246
Spatially resolved single-cell genomics and transcriptomics by imaging
Zhuang, X. (2021) · Nature Methods · doi:10.1038/s41592-020-01037-8 · Cited by 243
Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq
Medaglia, C., Giladi, A., Stoler‐Barak, L., Giovanni, M. D., Salame, T., Biram, A., David, E., Li, H., Iannacone, M., Shulman, Z., et al. (2017) · Science · doi:10.1126/science.aao4277 · Cited by 239
Multiplexed Epitope-Based Tissue Imaging for Discovery and Healthcare Applications
Bodenmiller, B. (2016) · Cell Systems · doi:10.1016/j.cels.2016.03.008 · Cited by 235
Probabilistic cell typing enables fine mapping of closely related cell types in situ
Qian, X., Harris, K. D., Hauling, T., Nicoloutsopoulos, D., Muñoz‐Manchado, A. B., Skene, N., Hjerling‐Leffler, J., & Nilsson, M. F. (2019) · Nature Methods · doi:10.1038/s41592-019-0631-4 · Cited by 233
Peng, G., Suo, S., Chen, J., Chen, W., Liu, C. X., Fang, Y., Wang, R., Chen, S., Sun, N., Cui, G., et al. (2016) · Developmental Cell · doi:10.1016/j.devcel.2016.02.020 · Cited by 231
Dissection of intercellular communication using the transcriptome-based framework ICELLNET
Noël, F., Massenet-Regad, L., Carmi-Levy, I., Cappuccio, A., Grandclaudon, M., Trichot, C., Kieffer, Y., Mechta‐Grigoriou, F., & Soumelis, V. (2021) · Nature Communications · doi:10.1038/s41467-021-21244-x · Cited by 217
Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 214
Tchesnokova, V. L., Kulasekara, H. D., Larson, L., Bowers, V., Rechkina, E., Kisiela, D., Sledneva, Y., Choudhury, D., Maslova, I., Deng, K., et al. (2021) · Journal of Clinical Microbiology · doi:10.1128/jcm.00921-21 · Cited by 214
Exuberant fibroblast activity compromises lung function via ADAMTS4
Boyd, D. F., Allen, E. K., Randolph, A. G., Guo, X.-Z. J., Weng, Y., Sanders, C. J., Bajracharya, R., Lee, N. K., Guy, C. S., Vogel, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2877-5 · Cited by 211
SpaGE: Spatial Gene Enhancement using scRNA-seq
Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa740 · Cited by 207
Spatial sorting enables comprehensive characterization of liver zonation
Ben-Moshe, S., Shapira, Y., Moor, A. E., Halpern, K. B., & Itzkovitz, S. (2019) · doi:10.1101/529784 · Cited by 205
Metastatic-niche labelling reveals parenchymal cells with stem features
Ombrato, L., Nolan, E., Kurelac, I., Mavousian, A., Bridgeman, V. L., Heinze, I., Chakravarty, P., Horswell, S. D., González‐Gualda, E., Matacchione, G., et al. (2019) · Nature · doi:10.1038/s41586-019-1487-6 · Cited by 205
Spatial sorting enables comprehensive characterization of liver zonation
Ben-Moshe, S., Shapira, Y., Moor, A. E., Manco, R., Veg, T., Bahar Halpern, K., & Itzkovitz, S. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0109-9 · Cited by 205
In situ genome sequencing resolves DNA sequence and structure in intact biological samples
Payne, A. C., Chiang, Z., Reginato, P., Mangiameli, S. M., Murray, E. M., Yao, C., Markoulaki, S., Earl, A., Labade, A. S., Jaenisch, R., et al. (2021) · Science · doi:10.1126/science.aay3446 · Cited by 199
Single-molecule mRNA detection and counting in mammalian tissue
Lyubimova, A., Itzkovitz, S., Junker, J. P., Fan, Z. P., Wu, X., & van Oudenaarden, A. (2013) · Nature Protocols · doi:10.1038/nprot.2013.109 · Cited by 198
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 195
GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
Yuan, Y., & Bar-Joseph, Z. (2020) · Genome Biology · doi:10.1186/s13059-020-02214-w · Cited by 194
Wu, C. C., Kruse, F., Vasudevarao, M. D., Junker, J. P., Zebrowski, D. C., Fischer, K. J., Noël, E. S., Grün, D., Berezikov, E., Engel, F. B., et al. (2015) · Developmental Cell · doi:10.1016/j.devcel.2015.12.010 · Cited by 192
Super-resolved spatial transcriptomics by deep data fusion
Bergenstråhle, L., He, B., Bergenstråhle, J., Abalo, X., Mirzazadeh, R., Thrane, K., Ji, A. L., Andersson, A., Larsson, L., Stakenborg, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01075-3 · Cited by 188
Self-citationFast, sensitive, and accurate integration of single cell data with HarmonyPreprint
Korsunsky, I., Fan, J., Slowikowski, K., et al. (2018) · bioRxiv · doi:10.1101/461954 · Cited by 177
Choi, H., Sheng, J., Gao, D., Li, F., Durrans, A., Ryu, S., Lee, S. B., Narula, N., Rafii, S., Elemento, O., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.01.040 · Cited by 168
Method of the Year 2020: spatially resolved transcriptomics
(2021) · Nature Methods · doi:10.1038/s41592-020-01042-x · Cited by 167
SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
Cao, Y., Wang, X., & Peng, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00490 · Cited by 166
Cell lineage and communication network inference via optimization for single-cell transcriptomics
Wang, S., Karikomi, M. K., MacLean, A. L., & Nie, Q. H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz204 · Cited by 157
Spatial transcriptomics: paving the way for tissue-level systems biology
Moor, A. E., & Itzkovitz, S. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.004 · Cited by 155
ZipSeq: barcoding for real-time mapping of single cell transcriptomes
Hu, K. H., Eichorst, J. P., McGinnis, C. S., Patterson, D. M., Chow, E. D., Kersten, K., Jameson, S. C., Gartner, Z. J., Rao, A. A., & Krummel, M. F. (2020) · Nature Methods · doi:10.1038/s41592-020-0880-2 · Cited by 155
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L. L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al. (2022) · Apollo (University of Cambridge) · Cited by 154
Ren, X., Zhong, G., Zhang, Q., Zhang, L., Sun, Y., & Zhang, Z. (2020) · Cell Research · doi:10.1038/s41422-020-0353-2 · Cited by 152
Deep profiling of mouse splenic architecture with CODEX multiplexed imagingPreprint
Goltsev, Y., Samusik, N., Kennedy‐Darling, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/203166 · Cited by 148
Giotto, a toolbox for integrative analysis and visualization of spatial expression dataPreprint
Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/701680 · Cited by 143
From whole-mount to single-cell spatial assessment of gene expression in 3D
Waylen, L. N., Nim, H. T., Martelotto, L. G., & Ramialison, M. (2020) · Communications Biology · doi:10.1038/s42003-020-01341-1 · Cited by 137
Dissecting mammalian spermatogenesis using spatial transcriptomics
Chen, H., Murray, E. M., Sinha, A., Laumas, A., Li, J., Lesman, D., Nie, X., Hotaling, J., Guo, J., Cairns, B. R., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109915 · Cited by 136
SpatialDB: a database for spatially resolved transcriptomes
Fan, Z., Chen, R., & Chen, X. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz934 · Cited by 128
NASC-seq monitors RNA synthesis in single cells
Hendriks, G., Jung, L. A., Larsson, A. J. M., Lidschreiber, M., Forsman, O. A., Lidschreiber, K., Cramer, P., & Sandberg, R. (2019) · Nature Communications · doi:10.1038/s41467-019-11028-9 · Cited by 113
Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging
Levenson, R. M., Borowsky, A. D., & Angelo, M. R. (2015) · Laboratory Investigation · doi:10.1038/labinvest.2015.2 · Cited by 112
AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
Aliee, H., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.006 · Cited by 103
Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control
Brosch, M., Kattler, K., Herrmann, A., von Schönfels, W., Nordström, K., Seehofer, D., Damm, G., Becker, T., Zeissig, S., Nehring, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06611-5 · Cited by 98
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Lomakin, A., Kedlian, V. R., Jain, M. S., Park, J. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.378125 · Cited by 88
Protective activity of mRNA vaccines against ancestral and variant SARS-CoV-2 strains
Ying, B., Whitener, B., VanBlargan, L. A., Hassan, A. O., Shrihari, S., Liang, C.-Y., Karl, C. E., Mackin, S., Chen, R. E., Kafai, N. M., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abm3302 · Cited by 85
SC3 - consensus clustering of single-cell RNA-Seq data
Kiselev, V. Y., Schaub, M. T., Andrews, T. S., Barahona, M., & Hemberg, M. (2015) · doi:10.7490/f1000research.1110908.1 · Cited by 80
Spatially resolved transcriptomics and its applications in cancer
Maniatis, S., Petrescu, J., & Phatnani, H. P. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.12.002 · Cited by 80
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arraysPreprint
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Li, W., Xu, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.17.427004 · Cited by 78
Whole-body tracking of single cells via positron emission tomography
Jung, K. O., Kim, T. J., Yu, J. H., Rhee, S., Zhao, W., Ha, B. H., Red‐Horse, K., Gambhir, S. S., & Pratx, G. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0570-5 · Cited by 77
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
Sexually transmitted herpes simplex viruses
Jonsson, M. K., & Wahrén, B. (2004) · Scandinavian Journal of Infectious Diseases · doi:10.1080/00365540310018905 · Cited by 69
RNA timestamps identify the age of single molecules in RNA sequencing
Rodriques, S. G., Chen, L. M., Liu, S., Zhong, E. D., Scherrer, J. R., Boyden, E. S., & Chen, F. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0704-z · Cited by 55
Burkhard, S. B., & Bakkers, J. (2018) · eLife · doi:10.7554/elife.31515 · Cited by 50
Moor, A. E., Harnik, Y., Ben‐Moshe, S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/261529 · Cited by 50
Rimm, D. L. (2014) · Nature Methods · doi:10.1038/nmeth.2896 · Cited by 47
SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomicsPreprint
Cabello‐Aguilar, S., Tack, F. K. S., Alame, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.11.872895 · Cited by 43
Super-resolved spatial transcriptomics by deep data fusionPreprint
Bergenstråhle, L., He, B. D., Bergenstråhle, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.28.963413 · Cited by 43
Peng, L., Hu, Y., Mankowski, M. C., Ren, P., Chen, R. E., Wei, J., Zhao, M., Li, T., Tripler, T. N., Ye, L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29288-3 · Cited by 43
Imaging Trans-Cellular Neurexin-Neuroligin Interactions by Enzymatic Probe Ligation
Liu, D. S., Loh, K. H., Lam, S. S., White, K. A., & Ting, A. Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0052823 · Cited by 42
Spatiotemporal Dynamics of Molecular Pathology in Amyotrophic Lateral SclerosisPreprint
Maniatis, S., Äijö, T., Vicković, S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/389270 · Cited by 41
deconvSeq: deconvolution of cell mixture distribution in sequencing data
Du, R., Carey, V. J., & Weiss, S. T. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz444 · Cited by 39
CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis
Xu, Y., & McCord, R. P. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04314-1 · Cited by 37
Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular RelationshipsPreprint
Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Ehinger, A., Wu, S. Z., Al‐Eryani, G., Roden, D. L., Swarbrick, A., Borg, Å., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.14.200600 · Cited by 36
Phillips, K. D. (1973) · PubMed · Cited by 29
Tanevski, J., Flores, R. O. R., Gábor, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.08.084145 · Cited by 28
SoRelle, E. D., Yecies, D. W., Liba, O., Bennett, F. C., Graef, C. M., Dutta, R., Mitra, S., Joubert, L.-M., Cheshier, S., Grant, G. A., et al. (2019) · ACS Nano · doi:10.1021/acsnano.9b02656 · Cited by 24
Splotch: Robust estimation of aligned spatial temporal gene expression dataPreprint
Äijö, T., Maniatis, S., Vicković, S., Kang, K., Cuevas, M., Braine, C., Phatnani, H. P., Lundeberg, J., & Bonneau, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/757096 · Cited by 21
Spatial Epigenome Sequencing at Tissue Scale and Cellular LevelPreprint
Deng, Y., Zhang, D., Liu, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.11.434985 · Cited by 20
[Problems of age-dependent differences in digestion].
Dietze, F., & Brüschke, G. (1970) · PubMed · Cited by 19
Fan, H., Su, B.-Y., Ma, C.-H., Rowley, P. A., & Jayaram, M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa401 · Cited by 13
Tanevski, J., Gábor, A., Flores, R. O. R., Schapiro, D., & Sáez-Rodríguez, J. (2021) · Research Square · doi:10.21203/rs.3.rs-735362/v1 · Cited by 12
Alugubelli, Y. R., Geng, Z. Z., Yang, K. S., Shaabani, N., Khatua, K., Ma, X. R., Vatansever, E. C., Cho, C.-C., Ma, Y., Blankenship, L. R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.18.473330 · Cited by 9
[Recurring fractures after osteosynthesis with compression plate].
Richon, A., Jj, L., & Saegesser, F. (1967) · PubMed · Cited by 6
Sun, Y., Dong, W., Tian, L., Rao, Y., Qin, C., Jaramillo, S. A., Settles, E. W., Ma, S., Zhang, J., Kang, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.07.447286 · Cited by 5
Research works printed but not linked: 3
- Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021). · DOI
- Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). · DOI · Paper page
- Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).
Cited by
Sorted by citations
CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics
Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448
Hu, C., Li, T., Xu, Y., Zhang, X., Li, F., Bai, J., Chen, J., Jiang, W., Yang, K., Ou, Q., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac947 · Cited by 1,373
Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 757
Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer
Raghavan, S., Winter, P. S., Navia, A. W., Williams, H. L., DenAdel, A., Lowder, K. E., Galvez-Reyes, J., Kalekar, R. L., Mulugeta, N., Kapner, K. S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.017 · Cited by 592
Dynamics and specificities of T cells in cancer immunotherapy
Oliveira, G., & Wu, C. J. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00560-y · Cited by 574
Metadata
- Authors
- Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A.
- Affiliations
- Stanford Cancer Institute (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Khavari, Paul A.
- Venue
- Nature Reviews Genetics
- Published
- 2021-06-18
- Volume
- 22
- Issue
- 10
- Pages
- 627-644
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC9888017
- OpenAlex
- W3171692328
- Cited by
- 1,059
- References
- 183
- Citations by year
- 2026: 233 · 2025: 203 · 2024: 242 · 2023: 196 · 2022: 157 · 2021: 27
- FWCI
- 16.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (R01CA142635)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR049737, R01AR043799, R01AR076965)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-cell RNA sequencing, spatial transcriptomics, data integration, intercellular communication, tissue dynamics, in situ sequencing, cell localization
- MeSH terms
- Animals, Cell Communication, Humans, Software, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis, Transcriptome
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