Single-cell transcriptome sequencing: recent advances and remaining challenges

RNA-Seq: a revolutionary tool for transcriptomics

Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174

Circular RNAs are a large class of animal RNAs with regulatory potency

Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., et al. (2013) · Nature · doi:10.1038/nature11928 · Cited by 7,990

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

MicroRNAs: small RNAs with a big role in gene regulation

He, L., & Hannon, G. J. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1379 · Cited by 7,118

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

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

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

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

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

Detecting and characterizing circular RNAs

Jeck, W. R., & Sharpless, N. E. (2014) · Nature Biotechnology · doi:10.1038/nbt.2890 · Cited by 2,710

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

Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593

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

Single-cell chromatin accessibility reveals principles of regulatory variation

Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308

Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing

Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.3881 · Cited by 2,136

RNA sequencing: advances, challenges and opportunities

Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135

Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types

Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795

Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550

The Technology and Biology of Single-Cell RNA Sequencing

Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487

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

Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345

Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq

Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248

Single-cell messenger RNA sequencing reveals rare intestinal cell types

Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234

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

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190

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

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell

Zong, C., Lu, S., Chapman, A. R., & Xie, X. S. (2012) · Science · doi:10.1126/science.1229164 · Cited by 1,154

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136

Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity

Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125

A Brief Review on the Mechanisms of miRNA Regulation

Cai, Y., Yu, X., Hu, S., & Yu, J. M. (2009) · Genomics Proteomics & Bioinformatics · doi:10.1016/s1672-0229(08)60044-3 · Cited by 1,031

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 RNA-seq reveals dynamic paracrine control of cellular variation

Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) · Nature · doi:10.1038/nature13437 · Cited by 1,000

Accounting for technical noise in single-cell RNA-seq experiments

Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979

Highly Multiplexed Subcellular RNA Sequencing in Situ

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973

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

Mammalian genes are transcribed with widely different bursting kinetics

Suter, D. M., Molina, N., Gatfield, D., Schneider, K., Schibler, U., & Naef, F. (2011) · Science · doi:10.1126/science.1198817 · Cited by 927

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

Single-cell RNA-seq: advances and future challenges

Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847

Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase

Buganim, Y., Faddah, D. A., Cheng, A. W., Itskovich, E., Markoulaki, S., Ganz, K., Klemm, S. L., van Oudenaarden, A., & Jaenisch, R. (2012) · Cell · doi:10.1016/j.cell.2012.08.023 · Cited by 793

How to infer gene networks from expression profiles

Bansal, M., Belcastro, V., Ambesi‐Impiombato, A., & di Bernardo, D. (2007) · Molecular Systems Biology · doi:10.1038/msb4100120 · Cited by 789

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

Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury

Llorens-Bobadilla, E., Zhao, S., Baser, A., Saiz-Castro, G., Zwadlo, K., & Martin-Villalba, A. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.07.002 · Cited by 778

G&T-seq: parallel sequencing of single-cell genomes and transcriptomes

Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750

Validation of noise models for single-cell transcriptomics

Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721

Synthetic spike-in standards for RNA-seq experiments

Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653

Advances and Applications of Single-Cell Sequencing Technologies

Wang, Y., & Navin, N. E. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.005 · Cited by 624

In the right place at the right time: visualizing and understanding mRNA localization

Buxbaum, A. R., Haimovich, G., & Singer, R. H. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3918 · Cited by 610

ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Pierson, E., & Yau, C. (2015) · Genome Biology · doi:10.1186/s13059-015-0805-z · Cited by 577

Mosaic Copy Number Variation in Human Neurons

McConnell, M. J., Lindberg, M. R., Brennand, K. J., Piper, J. C., Voet, T., Cowing-Zitron, C., Shumilina, S., Lasken, R. S., Vermeesch, J. R., Hall, I. M., et al. (2013) · Science · doi:10.1126/science.1243472 · Cited by 551

Single-Neuron Sequencing Analysis of L1 Retrotransposition and Somatic Mutation in the Human Brain

Evrony, G. D., Cai, X., Lee, E., Hills, L. B., Elhosary, P. C., Lehmann, H. S., Parker, J. J., Atabay, K. D., Gilmore, E. C., Poduri, A., et al. (2012) · Cell · doi:10.1016/j.cell.2012.09.035 · Cited by 548

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 methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing

Guo, H., Zhu, P., Wu, X., Li, X., Wen, L., & Tang, F. (2013) · Genome Research · doi:10.1101/gr.161679.113 · Cited by 518

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

Integrated genome and transcriptome sequencing of the same cell

Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502

From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Marinov, G. K., Williams, B. A., McCue, K., Schroth, G. P., Gertz, J., Myers, R. M., & Wold, B. J. (2014) · Genome Research · doi:10.1101/gr.161034.113 · Cited by 468

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

Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons

Krishnaswami, S. R., Grindberg, R. V., Novotny, M., Venepally, P., Lacar, B., Bhutani, K., Linker, S. B., Pham, S., Erwin, J. A., Miller, J. A., et al. (2016) · Nature Protocols · doi:10.1038/nprot.2016.015 · Cited by 445

Genomewide characterization of non-polyadenylated RNAs

Yang, L. C., Duff, M. O., Graveley, B. R., Carmichael, G., & Chen, L. (2011) · Genome biology · doi:10.1186/gb-2011-12-2-r16 · Cited by 436

Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics

Farlik, M., Sheffield, N. C., Nuzzo, A., Datlinger, P., Schönegger, A., Klughammer, J., & Bock, C. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.02.001 · Cited by 422

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

Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos

Fan, X., Zhang, X., Wu, X., Guo, H., Hu, Y., Tang, F., & Huang, Y. (2015) · Genome biology · doi:10.1186/s13059-015-0706-1 · Cited by 415

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

Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts714 · Cited by 395

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380

Deconstructing transcriptional heterogeneity in pluripotent stem cells

Kumar, R. M., Cahan, P., Shalek, A. K., Satija, R., Jay DaleyKeyser, A., Li, H., Zhang, J., Pardee, K., Gennert, D., Trombetta, J. J., et al. (2014) · Nature · doi:10.1038/nature13920 · Cited by 356

Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses

Avraham, R., Haseley, N., Brown, D., Peñaranda, C., Jijon, H. B., Trombetta, J. J., Satija, R., Shalek, A. K., Xavier, R. J., Regev, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.08.027 · Cited by 341

High‐throughput sequencing for biology and medicine

Soon, W. W., Hariharan, M., & Snyder, M. P. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.61 · Cited by 313

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

Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes

Shiroguchi, K., Jia, T. Z., Sims, P. A., & Xie, X. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1118018109 · Cited by 276

Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing

Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.022 · Cited by 272

Image-based transcriptomics in thousands of single human cells at single-molecule resolution

Battich, N., Stoeger, T., & Pelkmans, L. (2013) · Nature Methods · doi:10.1038/nmeth.2657 · Cited by 269

Single-Cell Transcriptome Analyses Reveal Signals to Activate Dormant Neural Stem Cells

Luo, Y., Coskun, V., Liang, A., Yu, J., Cheng, L., Ge, W., Shi, Z., Zhang, K., Li, C., Cui, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.001 · Cited by 264

Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2013) · Nature Biotechnology · doi:10.1038/nbt.2642 · Cited by 246

Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data

Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 216

Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids De Novo to Contribute to Immune Homeostasis

Mahata, B., Zhang, X., Kolodziejczyk, A. A., Proserpio, V., Haim-Vilmovsky, L., TAYLOR, A. E. R., Hebenstreit, D., Dingler, F. A., Moignard, V., Göttgens, B., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.04.011 · Cited by 214

DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution

Falconer, E., Hills, M., Naumann, U., Poon, S. S. S., Chavez, E. A., Sanders, A. D., Zhao, Y., Hirst, M., & Lansdorp, P. M. (2012) · Nature Methods · doi:10.1038/nmeth.2206 · Cited by 199

Review on statistical methods for gene network reconstruction using expression data

Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182

Digital transcriptome profiling using selective hexamer priming for cDNA synthesis

Armour, C. D., Castle, J. C., Chen, R., Babak, T., Loerch, P., Jackson, S., Shah, J. K., Dey, J., Rohl, C. A., Johnson, J. M., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1360 · Cited by 176

Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments

Leng, N., Chu, L., Barry, C., Li, Y., Choi, J., Li, X., Jiang, P., Stewart, R. M., Thomson, J. A., & Kendziorski, C. M. (2015) · Nature Methods · doi:10.1038/nmeth.3549 · Cited by 171

A method for counting PCR template molecules with application to next-generation sequencing

Casbon, J. A., Osborne, R. J., Brenner, S., & Lichtenstein, C. P. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr217 · Cited by 158

Counting individual DNA molecules by the stochastic attachment of diverse labels

Fu, G. K., Hu, J., Wang, P., & Fodor, S. P. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1017621108 · Cited by 142

Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

Geier, F. M., Timmer, J., & Fleck, C. C. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-11 · Cited by 122

Transcript amplification from single bacterium for transcriptome analysisPreprint

Kang, Y., Norris, M. H., Zarzycki‐Siek, J., Nierman, W. C., Donachie, S. P., & Hoang, T. T. (2011) · Genome Research · doi:10.1101/gr.116103.110 · Cited by 121

SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression

Nakamura, T., Yabuta, Y., Okamoto, I., Aramaki, S., Yokobayashi, S., Kurimoto, K., Sekiguchi, K., Nakagawa, M., Yamamoto, T., & Saitou, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv134 · Cited by 120

Single‐cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3‐ITD‐mutated AML stem/progenitor cells

Han, L., Qiu, P., Zeng, Z., Jorgensen, J. L., Mak, D. H. F., Burks, J. K., Schober, W. D., McQueen, T. J., Cortes, J. E., Tanner, S. D., et al. (2015) · Cytometry Part A · doi:10.1002/cyto.a.22628 · Cited by 100

Normalization and noise reduction for single cell RNA-seq experiments

Ding, B., Zheng, L., Zhu, Y., Li, N., Jia, H., Ai, R., Wildberg, A., & Wang, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv122 · Cited by 99

Application of single-cell genomics in cancer: promise and challenges

Wills, Q. F., & Mead, A. J. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv235 · Cited by 77

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Hurley, D., Araki, H., Tamada, Y., Dunmore, B., Sanders, D. A., Humphreys, S., Affara, M., Imoto, S., Yasuda, K., Tomiyasu, Y., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr902 · Cited by 76

Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems

Angelini, C., & Costa, V. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00051 · Cited by 69

Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq

Min, J.-W., Kim, W. J., Han, J. A., Jung, Y., Kim, K.-T., Park, W., Lee, H., & Choi, S. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135817 · Cited by 61

Single‐cell transcriptomics and functional target validation of brown adipocytes show their complex roles in metabolic homeostasis

Spaethling, J. M., Sánchez-Alavez, M., Lee, J., Xia, F. C., Dueck, H., Wang, W., Fisher, S. A., Sul, J., Seale, P., Kim, J., et al. (2015) · The FASEB Journal · doi:10.1096/fj.15-273797 · Cited by 54

Measurement of the Number of Molecules of a Single mRNA Species in a Complex mRNA Preparation

Hug, H., & Schuler, R. (2003) · Journal of Theoretical Biology · doi:10.1006/jtbi.2003.3211 · Cited by 42

Inferring single-cell gene expression mechanisms using stochastic simulation

Daigle, B. J., Soltani, M., Petzold, L. R., & Singh, A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv007 · Cited by 42

Using variability in gene expression as a tool for studying gene regulation

Padovan-Merhar, O. M., & Raj, A. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1243 · Cited by 42

Quantitative Transcriptomics using Designed Primer-based Amplification

Bhargava, V., Ko, P., Willems, E., Mercola, M. A., & Subramaniam, S. (2013) · Scientific Reports · doi:10.1038/srep01740 · Cited by 40

Single-cell, locus-specific bisulfite sequencing (SLBS) for direct detection of epimutations in DNA methylation patterns

Gravina, S., Ganapathi, S. S., & Vijg, J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv366 · Cited by 38

Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics

Roditi, L. D. V., & Claassen, M. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.10.010 · Cited by 36

Reconstructing gene networks: what are the limits?

Stark, J., Brewer, D. S., Barenco, M., Daniela, T., Callard, R. E., & Hubank, M. J. F. (2003) · Biochemical Society Transactions · doi:10.1042/bst0311519 · Cited by 32

A stochastic transcriptional switch model for single cell imaging data

Hey, K. L., Momiji, H., Featherstone, K., Davis, J. R., White, M., Rand, D. A. J., & Finkenstädt, B. (2015) · Biostatistics · doi:10.1093/biostatistics/kxv010 · Cited by 29

Digital Encoding of Cellular mRNAs Enabling Precise and Absolute Gene Expression Measurement by Single-Molecule Counting

Fu, G. K., Wilhelmy, J., Stern, D., Fan, H. C., & Fodor, S. P. A. (2014) · Analytical Chemistry · doi:10.1021/ac500459p · Cited by 28

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

Gene regulation by long non-coding RNAs and its biological functions

Statello, L., Guo, C.-J., Chen, L.-L., & Huarte, M. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-00315-9 · Cited by 4,991

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

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

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 sequencing leads a new era of profiling transcriptomic landscape

Zhang, H., Cui, N., Cai, Y., Lei, F., & Weitz, D. A. (2018) · Journal of Bio-X Research · doi:10.1097/jbr.0000000000000003 · Cited by 8

The Application of Single-Cell Sequencing in Dynamic Transcriptomes

Chen, K., Dong, X., & Wu, J. (2015) · Translational bioinformatics · doi:10.1007/978-94-017-9753-5_4 · Cited by 3

Single cell transcriptomics

(2015) · Wiley eBooks · doi:10.1002/9783527678679.dg11959 · Cited by 1

Single-cell transcriptomics: background, technologies, applications, and challenges

Duhan, L., Kumari, D., Naime, M., Parmar, V. S., Chhillar, A. K., Dangi, M., & Pasrija, R. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-09553-y · Cited by 29

Single-Cell Transcriptome Profiling

Shapira, G., & Shomron, N. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1103-6_16 · Cited by 10