From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
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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Single-cell RNA-seq enables the quantification of RNA abundance at the resolution of individual cells.
“Single-cell RNA-seq (scRNA-seq) enables the quantification of RNA abundance at the resolution of individual cells4.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [4]
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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 · see citing works
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 12,116 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 3,097 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,885 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,288 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationCEL-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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 936 · see citing works
Self-citationIn 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 · see citing works
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 · see citing works
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 · see citing works
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 855 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Zhu, Y., Machleder, E. M., Chenchik, A. A., Li, R., & Siebert, P. D. (2001) · BioTechniques · doi:10.2144/01304pf02 · Cited by 773 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 706 · see citing works
SpatialDE: identification of spatially variable genes
Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695 · see citing works
Self-citationError Correction Coding : Mathematical Methods and Algorithms
Moon, T. K. (2005) · Wiley eBooks · doi:10.1002/0471739219 · Cited by 678 · see citing works
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 677 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
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 · see citing works
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 · see citing works
Self-citationWishbone 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 · see citing works
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 557 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationTang, 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 499 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
T cell fate and clonality inference from single-cell transcriptomes
(2016) · doi:10.1038/nmeth.3800 · Cited by 434 · see citing works
Self-citationRNA-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 · see citing works
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 · see citing works
Kurimoto, K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl050 · Cited by 403 · see citing works
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 · see citing works
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 394 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 296 · see citing works
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 · see citing works
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 280 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationSingle-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 · see citing works
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 235 · see citing works
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 235 · see citing works
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 210 · see citing works
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 · see citing works
Self-citationAmini, 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 · see citing works
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 189 · see citing works
Kurimoto, K., Yabuta, Y., Ohinata, Y., & Saitou, M. (2007) · Nature Protocols · doi:10.1038/nprot.2007.79 · Cited by 186 · see citing works
Sensitive detection of rare disease-associated cell subsets via representation learning
Arvaniti, E., & Claassen, M. (2017) · Nature Communications · doi:10.1038/ncomms14825 · Cited by 176 · see citing works
Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation
Levesque, M. J., & Raj, A. (2013) · Nature Methods · doi:10.1038/nmeth.2372 · Cited by 151 · see citing works
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 · see citing works
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 · see citing works
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 131 · see citing works
Lü, P., Nakorchevsky, A., & Marcotte, E. M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1832361100 · Cited by 130 · see citing works
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 128 · see citing works
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 119 · see citing works
Schmidt, W. M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.21.e31 · Cited by 112 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationComputational 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 · see citing works
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 · see citing works
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 76 · see citing works
Self-citationA Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues
Mohammadi, S., Zuckerman, N., Goldsmith, A. J., & Grama, A. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2607121 · Cited by 74 · see citing works
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 · see citing works
Scaling single cell transcriptomics through split pool barcodingPreprint
Rosenberg, A. B., Roco, C. M., Muscat, R. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/105163 · Cited by 39 · see citing works
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 · see citing works
Boukouvalas, A., Hensman, J. J., & Rattray, M. (2018) · Genome biology · doi:10.1186/s13059-018-1440-2 · Cited by 28 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationPenfold, 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 · see citing works
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A scalable SCENIC workflow for single-cell gene regulatory network analysis
Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0336-2 · Cited by 1,494 · see citing works
Confronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,017 · see citing works
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 913 · see citing works
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 853 · see citing works
Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P. L., Nagai, J. S., Boys, C., Ramirez Flores, R. O., Kim, H., Szalai, B., Costa, I. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30755-0 · Cited by 664 · see citing works
Droplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works
Improved tools to study astrocytes
Yu, X., Nagai, J., & Khakh, B. S. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0264-8 · Cited by 298 · see citing works
Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
Bernstein, N. J., Fong, N. L., Lam, I., Roy, M. A., Hendrickson, D. G., & Kelley, D. R. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.010 · Cited by 280 · see citing works
The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease
Schiller, H. B., Montoro, D. T., Simon, L. M., Rawlins, E. L., Meyer, K. B., Strunz, M., Vieira Braga, F. A., Timens, W., Koppelman, G. H., Budinger, G. R. S., et al. (2019) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2018-0416tr · Cited by 245 · see citing works
Self-citationIntrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity
Rodriguez, J., Ren, G., Day, C. R., Zhao, K., Chow, C. C., & Larson, D. R. (2018) · Cell · doi:10.1016/j.cell.2018.11.026 · Cited by 267 · see citing works
Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019) · Genome biology · doi:10.1186/s13059-019-1898-6 · Cited by 225 · see citing works
Pichon, X., Lagha, M., Mueller, F., & Bertrand, É. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.07.022 · Cited by 181 · see citing works
The barcode, UMI, set format and BUStools
Melsted, P., Ntranos, V., & Pachter, L. S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz279 · Cited by 151 · see citing works
The embryonic transcriptome of Arabidopsis thaliana
Hofmann, F., Schon, M. A., & Nodine, M. D. (2019) · Plant Reproduction · doi:10.1007/s00497-018-00357-2 · Cited by 130 · see citing works
Cell composition analysis of bulk genomics using single-cell data
Frishberg, A., Peshes‐Yaloz, N., Cohn, O., Rosentul, D. C., Steuerman, Y., Valadarsky, L., Yankovitz, G., Mandelboim, M., Iraqi, F. A., Amit, I., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0355-5 · Cited by 117 · see citing works
Tang, W., Bertaux, F., Thomas, P., Stefanelli, C., Saint, M., Marguerat, S., & Shahrezaei, V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz726 · Cited by 113 · see citing works
Wohnhaas, C. T., Leparc, G. G., Fernandez-Albert, F., Kind, D., Gantner, F., Viollet, C., Hildebrandt, T., & Baum, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-46932-z · Cited by 105 · see citing works
FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants
Long, Y., Liu, Z., Jia, J., Mo, W., Fang, L., Lu, D., Liu, B., Zhang, H., Chen, W., & Zhai, J. (2021) · Genome biology · doi:10.1186/s13059-021-02288-0 · Cited by 108 · see citing works
SpiceMix enables integrative single-cell spatial modeling of cell identity
Chidester, B., Zhou, T., Alam, S., & Ma, J. (2023) · Nature Genetics · doi:10.1038/s41588-022-01256-z · Cited by 100 · see citing works
Chemical Analysis of Single Cells
Oomen, P. E., Amiri-Aref, M., Kaya, I., Phan, N. T. N., & Ewing, A. G. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b04732 · Cited by 96 · see citing works
Diversity of Reactive Astrogliosis in CNS Pathology: Heterogeneity or Plasticity?
Moulson, A. J., Squair, J. W., Franklin, R. J. M., Tetzlaff, W., & Assinck, P. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.703810 · Cited by 90 · see citing works
Computational Methods for Single-Cell RNA Sequencing
Hie, B., Peters, J. M., Nyquist, S. K., Shalek, A. K., Berger, B., & Bryson, B. D. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-012220-100601 · Cited by 87 · see citing works
Liu, C., Wu, T., Fei, F., Liu, Y., Wu, L., Junkin, M., Wang, Z., Yu, Y., Wang, W., Wei, W., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/818450 · Cited by 88 · see citing works
Mapping human cell phenotypes to genotypes with single-cell genomics
Camp, J. G., Platt, R. J., & Treutlein, B. (2019) · Science · doi:10.1126/science.aax6648 · Cited by 83 · see citing works
Feleke, R., Reynolds, R. H., Smith, A. M., Tilley, B. S., Taliun, S. A. G., Hardy, J., Matthews, P. M., Gentleman, S., Owen, D. R., Johnson, M. R., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02343-x · Cited by 68 · see citing works
A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines
Nayak, R., & Hasija, Y. (2021) · Genomics · doi:10.1016/j.ygeno.2021.01.007 · Cited by 58 · see citing works
Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives
Luo, G., Gao, Q., Zhang, S., & Yan, B. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.016 · Cited by 59 · see citing works
HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging
Safieddine, A., Coleno, E., Lionneton, F., Traboulsi, A.-M., Salloum, S., Lecellier, C., Gostan, T., Georget, V., Hassen‐Khodja, C., Imbert, A., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00750-2 · Cited by 56 · see citing works
Sultana, A., Alam, M. S., Liu, X., Sharma, R., Singla, R. K., Gundamaraju, R., & Shen, B. (2022) · Translational Oncology · doi:10.1016/j.tranon.2022.101571 · Cited by 53 · see citing works
van Kuijk, K., Kuppe, C., Betsholtz, C., Vanlandewijck, M., Kramann, R., & Sluimer, J. C. (2019) · Cardiovascular Research · doi:10.1093/cvr/cvz185 · Cited by 52 · see citing works
Gupta, A., Shamsi, F., Altemose, N., Dorlhiac, G. F., Cypess, A. M., White, A. P., Yosef, N., Patti, M., Tseng, Y.-H., & Streets, A. (2022) · Genome Research · doi:10.1101/gr.275509.121 · Cited by 48 · see citing works
Drosophila PDGF/VEGF signaling from muscles to hepatocyte-like cells protects against obesity
Ghosh, A. C., Tattikota, S. G., Liu, Y., Comjean, A., Hu, Y., Barrera, V., Sui, S. J. H., & Perrimon, N. (2020) · eLife · doi:10.7554/elife.56969 · Cited by 46 · see citing works
Aquila: a spatial omics database and analysis platform
Zheng, Y., Chen, Y., Ding, X., Wong, K. H., & Cheung, E. C. W. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac874 · Cited by 42 · see citing works
The Stochastic Genome and Its Role in Gene ExpressionPreprint
Bohrer, C. H., & Larson, D. R. (2021) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a040386 · Cited by 42 · see citing works
Yin, K., Zhao, M., Lin, L., Chen, Y., Huang, S., Zhu, C., Liang, X., Lin, F., Wei, H., Zeng, H., et al. (2022) · Small Methods · doi:10.1002/smtd.202200341 · Cited by 36 · see citing works
High-dimensional analysis of intestinal immune cells during helminth infection
Ferrer‐Font, L., Mehta, P. H., Harmos, P., Schmidt, A., Chappell, S., Price, K. M., Hermans, I. F., Ronchese, F., Gros, G. S. L., & Mayer, J. U. (2020) · eLife · doi:10.7554/elife.51678 · Cited by 35 · see citing works
Investigation ofBrassicaand its relative genomes in the post-genomics era
Wu, J., Liang, J., Lin, R., Cai, X., Zhang, L., Guo, X., Wang, T., Chen, H., & Wang, X. (2022) · Horticulture Research · doi:10.1093/hr/uhac182 · Cited by 37 · see citing works
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination
Laturnus, S., Kobak, D., & Berens, P. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09461-z · Cited by 30 · see citing works
Chen, D., Zaid, M. I. A., Reiter, J. L., Czader, M., Wang, L., McGuire, P. C., Xuei, X., Gao, H., Huang, K., Abonour, R., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.663487 · Cited by 23 · see citing works
Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data
You, Y., Dong, X., Wee, Y. K., Maxwell, M. J., Alhamdoosh, M., Smyth, G. K., Hickey, P. F., Ritchie, M. E., & Law, C. W. (2023) · Genome biology · doi:10.1186/s13059-023-02949-2 · Cited by 23 · see citing works
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 · see citing works
Berger, E., Yörükoğlu, D., Zhang, L., Nyquist, S. K., Shalek, A. K., Kellis, M., Numanagić, I., & Berger, B. (2020) · Nature Communications · doi:10.1038/s41467-020-18320-z · Cited by 20 · see citing works
Krull, K. K., Ali, S. A., & Krijgsveld, J. (2024) · Nature Communications · doi:10.1038/s41467-024-52605-x · Cited by 17 · see citing works
Cell Type–Specific Expression Analysis of the Inner Ear: A Technical Report
Hertzano, R. P., Gwilliam, K., Rose, K. P., Milon, B. C., & Matern, M. S. (2020) · The Laryngoscope · doi:10.1002/lary.28765 · Cited by 17 · see citing works
Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology
London, C. A., Gardner, H. L., Zhao, S., Knapp, D. W., Utturkar, S. M., Duval, D. L., Chambers, M. R., Ostrander, E. A., Trent, J. M., & Kuffel, G. R. (2023) · Veterinary and Comparative Oncology · doi:10.1111/vco.12935 · Cited by 18 · see citing works
Simão, D. F., Gomes, C. M., Alves, P. M., & Brito, C. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107883 · Cited by 14 · see citing works
Advances of single-cell genomics and epigenomics in human disease: where are we now?
Kamies, R., & Martinez‐Jimenez, C. P. (2020) · Mammalian Genome · doi:10.1007/s00335-020-09834-4 · Cited by 13 · see citing works
Phenotype Identification of HeLa Cells Knockout CDK6 Gene Based on Label-Free Raman Imaging
Xu, Y. H., Hou, X. S., Zhu, Q., Mao, S., Ren, J., Lin, J., & Xu, N. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c00188 · Cited by 12 · see citing works
Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Li, Z., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.4 · Cited by 11 · see citing works
Gao, C., Pu, H., Zhou, Q., Tao, T., Liu, H., Sun, X., He, X., & Xiao, J. (2021) · Aging · doi:10.18632/aging.202857 · Cited by 11 · see citing works
SPICEMIX: Integrative single-cell spatial modeling of cell identityPreprint
Chidester, B., Zhou, T., Alam, S., & Ma, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.29.383067 · Cited by 11 · see citing works
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 · see citing works
Xu, Q., Zhang, Y., Xu, W. Q., Liu, D., Jin, W., Chen, X. X., & Hong, N. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae095 · Cited by 8 · see citing works
Self-citationDeciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2023) · eLife · doi:10.7554/elife.88294 · Cited by 7 · see citing works
Jeong, M., Choi, J., Jang, H., Sohn, D. H., Wang, Q., Lee, J., Li, Y., Lee, E. J., Fan, J., Pratelli, M., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.038 · Cited by 7 · see citing works
The Human Lung Cell Atlas: a transformational resource for cells of the respiratory system
Meyer, K. B., Wilbrey-Clark, A., Nawijn, M. C., & Teichmann, S. A. (2021) · European Respiratory Society eBooks · doi:10.1183/2312508x.10010920 · Cited by 7 · see citing works
Self-citationSeq-ing out cell types across the isocortex and hippocampal formation
Ming, G., & Song, H. (2021) · Cell · doi:10.1016/j.cell.2021.05.016 · Cited by 7 · see citing works
Scalable integration of multiomic single-cell data using generative adversarial networks
Giansanti, V., Giannese, F., Botrugno, O. A., Gandolfi, G., Balestrieri, C., Antoniotti, M., Tonon, G., & Cittaro, D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae300 · Cited by 6 · see citing works
Welch, E. C., Chaltas, K., & Tripathi, A. (2023) · SLAS TECHNOLOGY · doi:10.1016/j.slast.2023.01.001 · Cited by 6 · see citing works
Chen, C., Leung, Y. Y., Ionita, M., Wang, L., & Li, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac563 · Cited by 6 · see citing works
A systematic evaluation of interneuron morphology representations for cell type discriminationPreprint
Laturnus, S., Kobak, D., & Berens, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/591370 · Cited by 6 · see citing works
Zhao, Z., Cui, H., & Cui, H. (2025) · Journal of Materials Chemistry B · doi:10.1039/d5tb00744e · Cited by 5 · see citing works
Wrobel, T. J., Brilhaus, D., Stefanski, A., Stühler, K., Weber, A. P. M., & Linka, N. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1182105 · Cited by 5 · see citing works
A Modified SMART-Seq Method for Single-Cell Transcriptomic Analysis of Embryoid Body Differentiation
Zheng, J., Ye, Y., Xu, Q., Xu, W. Q., Zhang, W., & Chen, X. X. (2021) · Methods in molecular biology · doi:10.1007/7651_2021_435 · Cited by 5 · see citing works
Self-citationPollo, S. M. J., Liu, H., Leon‐Coria, A., Rosin, N. L., Labit, E., Biernaskie, J. A., Finney, C. A. M., & Wasmuth, J. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.27.582282 · Cited by 5 · see citing works
Novel Technologies in Studying Brain Immune Response
Li, L., Lenahan, C., Zhihui, L., Ke, J.-D., Li, X., Xue, F., & Zhang, J. H. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/6694566 · Cited by 3 · see citing works
Hutchison, L. A. D., Berger, B., & Kohane, I. S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz864 · Cited by 4 · see citing works
Solo: doublet identification via semi-supervised deep learningPreprint
Bernstein, N., Fong, N. L., Lam, I., Roy, M. A., Hendrickson, D. G., & Kelley, D. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/841981 · Cited by 4 · see citing works
Single-cell analysis in endometrial research
Cao, D., Wang, J., Yao, Y., & Yeung, W. S. B. (2022) · Reproductive and Developmental Medicine · doi:10.1097/rd9.0000000000000037 · Cited by 3 · see citing works
Collí‐Dulá, R. C., & Papatheodorou, I. (2024) · Comparative Biochemistry and Physiology Part D Genomics and Proteomics · doi:10.1016/j.cbd.2024.101339 · Cited by 4 · see citing works
Planchak, S., Welch, E. C., Phelps, B., Phelps, J., Moyers, A. H., Whitehead, K., Murphy, J., Tapinos, N., & Tripathi, A. (2025) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-025-00850-5 · Cited by 3 · see citing works
Gupta, A., Shamsi, F., Altemose, N., Dorlhiac, G., Cypess, A. M., White, A. P., Patti, M., Tseng, Y., & Streets, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.24.435852 · Cited by 3 · see citing works
The role of miR-21 in early-stage lung adenocarcinoma: clinical and bioinformatics insights
Qin, T., & Zhang, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-21742-8 · Cited by 2 · see citing works
Rombouts, S., & Nöllmann, M. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa051 · Cited by 2 · see citing works
Bentzur, A., Alon, S., & Shohat-Ophir, G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073811 · Cited by 2 · see citing works
Question-driven stepwise experimental discoveries in biochemistry: two case studies
Fry, M. (2022) · History & Philosophy of the Life Sciences · doi:10.1007/s40656-022-00491-1 · Cited by 2 · see citing works
UNCURL-App: Interactive database-driven analysis of scRNA-Seq dataPreprint
Zhang, Y., Mao, S., Mukherjee, S., Kannan, S., & Seelig, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.043737 · Cited by 2 · see citing works
Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA- sequencingPreprint
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Li, Z., Lian, C., et al. (2023) · eLife · doi:10.7554/elife.88294.1 · Cited by 2 · see citing works
Early Alzheimer’s Prediction Using Dimensionality Reduction Techniques
Paplomatas, P., & Vrahatis, A. G. (2023) · Springer eBooks · doi:10.1007/978-3-319-75922-7_65 · Cited by 1 · see citing works
Induced pluripotent stem cells as models of human neurodevelopmental disorders
Jourdon, A., Mariani, J., Scuderi, S., Amiri, A., Wu, F., Yuen, E. Y., Abyzov, A. S., & Vaccarino, F. M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814409-1.00005-7 · Cited by 1 · see citing works
RNA-Seq Analysis: Methods, Applications and Challenges
vari, A., Geraci, F., Saha, I., & Bianchini, M. (2020) · Frontiers research topics · doi:10.3389/978-2-88963-705-8 · Cited by 1 · see citing works
Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencingPreprint
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.29.542712 · Cited by 1 · see citing works
Approximate kNN Classification for Biomedical Data
Anagnostou, P., Barbas, P., Vrahatis, A. G., & Tasoulis, S. K. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378126 · Cited by 15 · see citing works
Decoding the heterogeneity of skin in homeostasis and regeneration at single-cell resolution
Joost, S. (2018) · Figshare
Deep Denoising Subspace Single-Cell Clustering
Wang, Y., & Yang, B. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63823-8_36
Singh, A. N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.24.445309
Wu, H., Li, J., Li, X., Gao, M., Tian, W., Chen, D., Zhou, Z., & Liu, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1837739/v1
Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq DataPreprint
Kleinberg, G., Shinde, A., Batchu, S., Diaz, M. J., Root, K. T., & Lucke-Wold, B. P. (2022) · Preprints.org · doi:10.20944/preprints202209.0327.v1
Early Alzheimer’s Prediction Using Dimensionality Reduction Techniques
Paplomatas, P., & Vrahatis, A. G. (2023) · Springer eBooks · doi:10.1007/978-3-319-75479-6_65-1
Krull, K. K., Ali, S. A., & Krijgsveld, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.10.575010
Long, Y., Liu, Z., Jia, J., Mo, W., Fang, L., Lu, D., Liu, B., Zhang, H., Chen, W., & Zhai, J. (2021) · Figshare · doi:10.6084/m9.figshare.14067995
Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencingPreprint
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Li, Z., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.2
Author response: Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.3.sa0
eLife Assessment: Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing
Paz‐García, D. A. (2024) · eLife · doi:10.7554/elife.88294.3.sa4
- Authors
- Chen, X., Teichmann, S. A., & Meyer, K. B.
- Affiliations
- Wellcome Sanger Institute (GB)
- European Bioinformatics Institute (GB)
- 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)
- 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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