Cited by 112Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

Chen, Xi, Teichmann, Sarah A., Meyer, Kerstin B.·2018·Annual Review of Biomedical Data Science

DOI 10.1146/annurev-biodatasci-080917-013452

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Abstract

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

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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
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
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
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
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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
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
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
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
A simple and very efficient method for generating cDNA libraries
Gubler, U., & Hoffman, B. J. (1983) · Gene · doi:10.1016/0378-1119(83)90230-5 · Cited by 4,461
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
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
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
The prognostic landscape of genes and infiltrating immune cells across human cancers
Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., et al. (2015) · Nature Medicine · doi:10.1038/nm.3909 · Cited by 3,163
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
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
Tumour evolution inferred by single-cell sequencing
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011) · Nature · doi:10.1038/nature09807 · Cited by 2,598
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
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Imaging individual mRNA molecules using multiple singly labeled probes
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008) · Nature Methods · doi:10.1038/nmeth.1253 · Cited by 2,028
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,904
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550
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
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
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348
A Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,307
Mass Cytometry: Technique for Real Time Single Cell Multitarget Immunoassay Based on Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
Bandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E., & Tanner, S. D. (2009) · Analytical Chemistry · doi:10.1021/ac901049w · Cited by 1,295
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231
A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · 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
Self-citation
CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq
Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125
Error Correction Coding
Moon, T. K. (2005) · Wiley eBooks · doi:10.1002/0471739219 · Cited by 1,107
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096
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
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069
Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos
Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S. P., Codeluppi, S., Plaza Reyes, A., Linnarsson, S., Sandberg, R., & Lanner, F. (2016) · Cell · doi:10.1016/j.cell.2016.03.023 · Cited by 1,063
Exponential scaling of single-cell RNA-seq in the past decade
Svensson, V., Vento-Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 1,034
Self-citation
Counting absolute numbers of molecules using unique molecular identifiers
Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. (2012) · Nature Methods · doi:10.1038/nmeth.1778 · Cited by 1,010
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
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
Xue, Z., Huang, K., Cai, C., Cai, L., Jiang, C.-Y., Feng, Y., Liu, Z., Zeng, Q., Cheng, L., Sun, Y. E., et al. (2013) · Nature · doi:10.1038/nature12364 · Cited by 963
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · 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
Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920
Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906
Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S. M., Love, J. C., & Shalek, A. K. (2017) · Nature Methods · doi:10.1038/nmeth.4179 · Cited by 882
Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst
Guo, G., Huss, M., Tong, G. Q., Wang, C., Sun, L., Clarke, N. D., & Robson, P. (2010) · Developmental Cell · doi:10.1016/j.devcel.2010.02.012 · Cited by 806
Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis
Shin, J., Berg, D. A., Zhu, Y., Shin, J. Y., Song, J., Bonaguidi, M. A., Enikolopov, G., Nauen, D. W., Christian, K. M., Ming, G.-L., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.07.013 · Cited by 784
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777
Reverse Transcriptase Template Switching: A SMART™ Approach for Full-Length cDNA Library Construction
Zhu, Y., Machleder, E. M., Chenchik, A. A., Li, R., & Siebert, P. D. (2001) · BioTechniques · doi:10.2144/01304pf02 · Cited by 773
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770
High-efficiency cloning of full-length cDNA.
Okayama, H., & Berg, P. (1982) · Molecular and Cellular Biology · doi:10.1128/mcb.2.2.161 · Cited by 765
Programmable in situ amplification for multiplexed imaging of mRNA expression
Choi, H. M. T., Chang, J. Y., Trinh, L. A., Padilla, J. E., Fraser, S. E., & Pierce, N. A. (2010) · Nature Biotechnology · doi:10.1038/nbt.1692 · Cited by 720
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., & Batzoglou, S. (2017) · Nature Methods · doi:10.1038/nmeth.4207 · Cited by 699
SpatialDE: identification of spatially variable genes
Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695
Self-citation
RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
Xin, Y., Kim, J., Okamoto, H., Ni, M., Wei, Y., Adler, C., Murphy, A. J., Yancopoulos, G. D., Lin, C., & Gromada, J. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.018 · Cited by 684
In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601
High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization
Moffitt, J. R., Hao, J., Wang, G., Chen, K. H., Babcock, H. P., & Zhuang, X. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612826113 · Cited by 568
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568
Self-citation
Wishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558
Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2014.191 · Cited by 529
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
Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell RNA-Seq Analysis
Tang, F., Bárbácioru, C., Bao, S., Lee, C. G. L., Nordman, E., Wang, X., Lao, K., & Surani, M. A. (2010) · Cell stem cell · doi:10.1016/j.stem.2010.03.015 · Cited by 515
Systematic evaluation of spliced alignment programs for RNA-seq data
The RGASP Consortium, Engström, P. G., Steijger, T., Sipos, B., Grant, G. R., Kahles, A., Rätsch, G., Goldman, N., Hubbard, T. J., Harrow, J., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2722 · Cited by 513
A test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511
Self-citation
Cell type–specific gene expression differences in complex tissues
Shen-Orr, S. S., Tibshirani, R. J., Khatri, P., Bodian, D. L., Staedtler, F., Perry, N. M., Hastie, T. J., Sarwal, M., Davis, M. M., & Butte, A. J. (2010) · Nature Methods · doi:10.1038/nmeth.1439 · Cited by 489
Single-cell systems biology by super-resolution imaging and combinatorial labeling
Lubeck, E., & Cai, L. (2012) · Nature Methods · doi:10.1038/nmeth.2069 · Cited by 462
Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity
Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r31 · Cited by 455
Combinatorial labeling of single cells for gene expression cytometry
Fan, H. C., Fu, G. K., & Fodor, S. P. A. (2015) · Science · doi:10.1126/science.1258367 · Cited by 455
T cell fate and clonality inference from single-cell transcriptomes
(2016) · doi:10.1038/nmeth.3800 · Cited by 434
Self-citation
Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
Everaert, C., Luypaert, M., Mååg, J. L. V., Cheng, Q. X., Dinger, M. E., Hellemans, J., & Mestdagh, P. (2017) · Scientific Reports · doi:10.1038/s41598-017-01617-3 · Cited by 430
RNA-sequencing from single nuclei
Grindberg, R. V., Yee-Greenbaum, J. L., McConnell, M. J., Novotny, M., O’Shaughnessy, A. L., Lambert, G. M., Araúzo-Bravo, M. J., Lee, J., Fishman, M., Robbins, G. E., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319700110 · Cited by 422
Nuclear RNA-seq of single neurons reveals molecular signatures of activation
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S. L., Paquola, A. C. M., Venepally, P., Novotny, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11022 · Cited by 405
An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis
Kurimoto, K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl050 · Cited by 403
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
Batch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356
Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq
Chen, J., Suo, S., Tam, P. P., Han, J.-D. J., Peng, G., & Jing, N. (2017) · Nature Protocols · doi:10.1038/nprot.2017.003 · Cited by 339
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
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
Jiang, L., Chen, H., Pinello, L., & Yuan, G. (2016) · Genome biology · doi:10.1186/s13059-016-1010-4 · Cited by 304
Computational deconvolution: extracting cell type-specific information from heterogeneous samples
Shen-Orr, S. S., & Gaujoux, R. (2013) · Current Opinion in Immunology · doi:10.1016/j.coi.2013.09.015 · Cited by 287
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
Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
Marco, E., Karp, R. L., Guo, G., Robson, P., Hart, A. H., Trippa, L., & Yuan, G.-C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408993111 · Cited by 268
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263
Single-Cell Transcriptional Analysis of Neuronal Progenitors
Tietjen, I., Rihel, J. M., Cao, Y., Koentges, G., Zakhary, L., & Dulac, C. (2003) · Neuron · doi:10.1016/s0896-6273(03)00229-0 · Cited by 260
Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves T H 1/T FH fate bifurcation in malaria
Lönnberg, T., Svensson, V., James, K. R., Fernandez‐Ruiz, D., Sebina, I., Montandon, R., Soon, M. S. F., Fogg, L. G., Nair, A. S., Liligeto, U. N., et al. (2017) · Science Immunology · doi:10.1126/sciimmunol.aal2192 · Cited by 260
Self-citation
Single-cell transcript analysis of pancreas development.
Chiang, M.-K., & Melton, D. (2003) · Developmental Cell · doi:10.1016/s1534-5807(03)00035-2 · Cited by 240
Challenges in long-term imaging and quantification of single-cell dynamics
Skylaki, S., Hilsenbeck, O., & Schroeder, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3713 · Cited by 240
Spatial Transcriptome for the Molecular Annotation of Lineage Fates and Cell Identity in Mid-gastrula Mouse Embryo
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
Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing
Shah, S., Lubeck, E., Schwarzkopf, M., He, T.-F., Greenbaum, A., Sohn, C. H., Lignell, A., Choi, H. M. T., Gradinaru, V., Pierce, N. A., et al. (2016) · Development · doi:10.1242/dev.138560 · Cited by 215
A benchmark for RNA-seq quantification pipelines
Teng, M., Love, M. I., Davis, C., Djebali, S., Dobin, A., Graveley, B. R., Li, S., Mason, C. E., Olson, S., Pervouchine, D. D., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0940-1 · Cited by 197
Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells
Macaulay, I. C., Svensson, V., Labalette, C., Ferreira, L., Hamey, F., Voet, T., Teichmann, S. A., & Cvejic, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.082 · Cited by 191
Self-citation
Haplotype-resolved whole-genome sequencing by contiguity-preserving transposition and combinatorial indexing
Amini, S., Pushkarev, D., Christiansen, L., Kostem, E., Royce, T., Turk, C., Pignatelli, N., Adey, A., Kitzman, J. O., Vijayan, K., et al. (2014) · Nature Genetics · doi:10.1038/ng.3119 · Cited by 189
Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis
Kurimoto, K., Yabuta, Y., Ohinata, Y., & Saitou, M. (2007) · Nature Protocols · doi:10.1038/nprot.2007.79 · Cited by 186
Sensitive detection of rare disease-associated cell subsets via representation learning
Arvaniti, E., & Claassen, M. (2017) · Nature Communications · doi:10.1038/ncomms14825 · Cited by 179
Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation
Levesque, M. J., & Raj, A. (2013) · Nature Methods · doi:10.1038/nmeth.2372 · Cited by 152
Scalable microfluidics for single-cell RNA printing and sequencing
Bose, S., Wan, Z., Carr, A., Rizvi, A. H., Vieira, G., Pe’er, D., & Sims, P. A. (2015) · Genome biology · doi:10.1186/s13059-015-0684-3 · Cited by 135
Separation of samples into their constituents using gene expression data
Venet, D., Pecasse, F., Maenhaut, C., & Bersini, H. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s279 · Cited by 135
Expression deconvolution: A reinterpretation of DNA microarray data reveals dynamic changes in cell populations
Lü, P., Nakorchevsky, A., & Marcotte, E. M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1832361100 · Cited by 135
Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads
Sasagawa, Y., Danno, H., Takada, H., Ebisawa, M., Tanaka, K. Y., Hayashi, T., Kurisaki, A., & Nikaido, I. (2018) · Genome biology · doi:10.1186/s13059-018-1407-3 · Cited by 135
An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq
Yuan, J., & Sims, P. A. (2016) · Scientific Reports · doi:10.1038/srep33883 · Cited by 129
Digital cell quantification identifies global immune cell dynamics during influenza infection
Altboum, Z., Steuerman, Y., David, E., Barnett‐Itzhaki, Z., Valadarsky, L., Keren‐Shaul, H., Meningher, T., Mendelson, E., Mandelboim, M., Gat‐Viks, I., et al. (2014) · Molecular Systems Biology · doi:10.1002/msb.134947 · Cited by 125
CapSelect: a highly sensitive method for 5' CAP-dependent enrichment of full-length cDNA in PCR-mediated analysis of mRNAs
Schmidt, W. M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.21.e31 · Cited by 112
Turbo FISH: A Method for Rapid Single Molecule RNA FISH
Shaffer, S. M., Wu, M.-T., Levesque, M. J., & Raj, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075120 · Cited by 110
A comparison of single-cell trajectory inference methods: towards more accurate and robust toolsPreprint
Saelens, W., Cannoodt, R., Todorov, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276907 · Cited by 98
BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq
Lindeman, I., Emerton, G., Mamanova, L., Snir, O., Polański, K., Qiao, S., Sollid, L. M., Teichmann, S. A., & Stubbington, M. J. T. (2018) · Nature Methods · doi:10.1038/s41592-018-0082-3 · Cited by 88
Self-citation
The Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 86
Self-citation
Computational expression deconvolution in a complex mammalian organ
Wang, M., Master, S. R., & Chodosh, L. A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-328 · Cited by 83
STRT-seq-2i: dual-index 5ʹ single cell and nucleus RNA-seq on an addressable microwell array
Hochgerner, H., Lönnerberg, P., Hodge, R. D., Mikeš, J., Heskol, A., Hubschle, H., Lin, P., Picelli, S., Manno, G. L., Ratz, M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-16546-4 · Cited by 78
A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues
Mohammadi, S., Zuckerman, N. S., Goldsmith, A. J., & Grama, A. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2607121 · Cited by 76
Single-cell transcriptomic characterization of 20 organs and tissues from individual mice creates a Tabula MurisPreprint
Schaum, N., Karkanias, J., Neff, N. F., May, A. P., Quake, S. R., Wyss‐Coray, T., Darmanis, S., Batson, J., Botvinnik, O. B., Chen, M. B., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/237446 · Cited by 50
Scaling single cell transcriptomics through split pool barcodingPreprint
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Mukherjee, S., Chen, W., Peeler, D. J., Yao, Z., Tasic, B., Sellers, D., et al. (2017) · bioRxiv · doi:10.1101/105163 · Cited by 43
Recovery of high-quality RNA from laser capture microdissected human and rodent pancreas
Butler, A. E., Matveyenko, A. V., Kirakossian, D., Park, J., Gurlo, T., & Butler, P. C. (2016) · Journal of Histotechnology · doi:10.1080/01478885.2015.1106073 · Cited by 31
BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
Boukouvalas, A., Hensman, J. J., & Rattray, M. (2018) · Genome biology · doi:10.1186/s13059-018-1440-2 · Cited by 29
Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighboursPreprint
Haghverdi, L., Lun, A. T. L., Morgan, M. D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165118 · Cited by 26
Assessment of batch-correction methods for scRNA-seq data with a new test metricPreprint
Büttner, M., Miao, Z., Wolf, F., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/200345 · Cited by 23
Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space
Okamura-Oho, Y., Shimokawa, K., Takemoto, S., Hirakiyama, A., Nakamura, S., Tsujimura, Y., Nishimura, M., Kasukawa, T., Masumoto, K., Nikaido, I., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045373 · Cited by 21
Integrating Results across Methodologies Is Essential for Producing Robust Neuronal Taxonomies
Cembrowski, M. S., & Spruston, N. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.023 · Cited by 17
SpatialDE - Identification of spatially variable genesPreprint
Svensson, V., Teichmann, S. A., & Stegle, O. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/143321 · Cited by 17
Self-citation
Bayesian inference of transcriptional branching identifies regulators of early germ cell development in humansPreprint
Penfold, C. A., Sybirna, A., Reid, J. E., Venzor, A. C., Drousioti, E., Huang, Y., Grant, M., Wernisch, L., Ghahramani, Z., & Surani, M. (2017) · bioRxiv · doi:10.1101/167684 · Cited by 5

Research works printed but not linked: 3

10.1126/science.aam8940 · DOI · Paper page
56. Boukouvalas A, Hensman J, Rattray M. 2017. BGP: branched Gaussian processes for identifying gene-specific branching dynamics in single cell data. bioRxiv 166868. http://doi:10.1101/166868 · DOI
Brady G, 1990, Methods Mol. Cell. Biol., 2, 17

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Authors: Chen, X., Teichmann, S. A., & Meyer, K. B.
Affiliations: Wellcome Sanger Institute (GB)
European Bioinformatics Institute (GB)
ORCID: Chen, Xi
Teichmann, Sarah A.
Meyer, Kerstin B.
Venue: Annual Review of Biomedical Data Science
Publisher: Annual Reviews
Published: 2018-07-20
Volume: 1
Issue: 1
Pages: 29-51
ISSN: 2574-3414 (ISSN-L)
DOI: 10.1146/annurev-biodatasci-080917-013452
OpenAlex: W2803942859
Cited by: 112
References: 127
Citations by year: 2026: 1 · 2025: 6 · 2024: 14 · 2023: 12 · 2022: 17 · 2021: 20 · 2020: 21 · 2019: 17 · 2018: 4
FWCI: 1.55
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Biomedical Data Science (publishedVersion)
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 › Genomics and Phylogenetic Studies
Keywords: single-cell RNA sequencing, tissue structure, cell types, spatial gene expression profiling, single-cell genomics, cell type annotation, spatial transcriptomics

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From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

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