The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
Open accessAbstract
Sequencing of RNA from thousands of individual immune cells allows unbiased identification of cellular subtypes.
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In the citing paper
“scRNA-seq has been used to deconvolve immune cell type heterogeneity by identifying novel distinct immune cell subsets in health and disease”
From Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (2019) · cites this paper as Jaitin et al., 2014
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“single-cell RNA sequencing (scRNA-seq) has emerged as a powerful method for unbiased discovery of cell types and states based on gene activity (Islam et al., 2011, Jaitin et al., 2014, Macosko et al., 2015, Ramsköld et al., 2012, Shekhar et al., 2016, Tang et al., 2009, Tasic et al., 2016, Usoskin et al., 2015, Zeisel et al., 2015)”
From Molecular Architecture of the Mouse Nervous System (2018) · cites this paper as Jaitin et al., 2014
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In the citing paper
“We estimated the level of spurious UMIs in the data using statistics on empty MARS-seq wells as previously described (Jaitin et al., 2014).”
From Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (2019) · cites this paper as Jaitin et al., 2014
Experimental — accuracy not yet measuredIn the citing paper
“The rationale is that by counting molecules rather than the number of amplified sequencing reads, one can account for biases related to amplification, and obtain more accurate estimates of gene expression levels71220.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [7]
In the citing paper
“When studying single cells, which have limited sample complexity, quantification is often carried out with just one million reads but may be done reliably for highly expressed genes with as few as 50,000 reads [8]; even 20,000 reads have been used to differentiate cell types in splenic tissue [9].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [9]
In the citing paper
“Imperfections in the capture, conversion, and amplification77,78 of the minute quantities of RNA in an individual cell into a cDNA library lead to challenges in regard to both false negatives (expressed but undetected transcripts) and false positives (transcripts with inflated expression levels) for individual transcripts, which may confound the understanding of biological variation.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [78]
In the citing paper
“Pre-sorting cells into wells can overcome these problems (Jaitin et al., 2014; Picelli et al., 2013) but at a cost in throughput.”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Jaitin et al., 2014
In the citing paper
“Several scRNA-seq methods developed in the recent decade have become essential for capturing intercellular variation and its impact on cell functions at the population scale15–19.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [17]
In the citing paper
“Other protocols have sacrificed full-length coverage in order to sequence part of the primer used for cDNA generation. This enables early barcoding of libraries (i.e., the incorporation of cell-specific barcodes), allowing for multiplexing the cDNA amplification and thereby increasing the throughput of scRNAseq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaiti”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Jaitin et al., 2014
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Citations
Cites
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
IDENTIFICATION OF A NOVEL CELL TYPE IN PERIPHERAL LYMPHOID ORGANS OF MICE
Steinman, R. M., & Cohn, Z. A. (1973) · The Journal of Experimental Medicine · doi:10.1084/jem.137.5.1142 · Cited by 2,443
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
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
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
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
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
Stochastic switching as a survival strategy in fluctuating environments
Açar, M., Mettetal, J. T., & van Oudenaarden, A. (2008) · Nature Genetics · doi:10.1038/ng.110 · Cited by 899
Single-cell dissection of transcriptional heterogeneity in human colon tumors
Dalerba, P. D., Tomer Kalisky, C., Sahoo, D., Rajendran, P. S., Rothenberg, M. E., Leyrat, A. A., Sim, S., Okamoto, J., Johnston, D. M., Qian, D., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.2038 · Cited by 704
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Items 101–169 of 169
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337
Highly multiplexed simultaneous detection of RNAs and proteins in single cells
Frei, A. P., Bava, F.-A., Zunder, E. R., Hsieh, E. W. Y., Chen, S.-Y., Nolan, G. P., & Gherardini, P. F. (2016) · Nature Methods · doi:10.1038/nmeth.3742 · Cited by 330
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320
Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity
Clark, S. J., Lee, H. J., Smallwood, S. A., Kelsey, G., & Reik, W. (2016) · Genome Biology · doi:10.1186/s13059-016-0944-x · Cited by 310
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
Ma, P., Amemiya, H. M., He, L. L., Gandhi, S. J., Nicol, R., Bhattacharyya, R. P., Smillie, C. S., & Hung, D. T. (2023) · Cell · doi:10.1016/j.cell.2023.01.002 · Cited by 291
Associating cellular epigenetic models with human phenotypes
Lappalainen, T., & Greally, J. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.32 · Cited by 282
Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Communications · doi:10.1038/ncomms9687 · Cited by 251
Disentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293
Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex
Johnson, M. B., Wang, P. P., Atabay, K. D., Murphy, E. A., Doan, R. N., Hecht, J. L., & Walsh, C. A. (2015) · Nature Neuroscience · doi:10.1038/nn.3980 · Cited by 288
Single-cell brain organoid screening identifies developmental defects in autism
Li, C., Fleck, J. S., Martins-Costa, C., Burkard, T. R., Themann, J., Stuempflen, M., Peer, A. M., Vertesy, Á., Littleboy, J. B., Esk, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06473-y · Cited by 283
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250
DestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276
“Single cells were sorted into 384-well plates and single cell RNA-seq libraries were generated using a modified version of the single cell MARS-seq protocol [92] [93].”
MethodSelf-citationClark, S. J., Smallwood, S. A., Lee, H. J., Krueger, F., Reik, W., & Kelsey, G. (2017) · Nature Protocols · doi:10.1038/nprot.2016.187 · Cited by 270
Deczkowska, A., Matcovitch-Natan, O., Tsitsou-Kampeli, A., Ben-Hamo, S., Dvir-Szternfeld, R., Spinrad, A., Singer, O., David, E., Winter, D. R., Smith, L. K., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00769-0 · Cited by 260
Self-citationSingle-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253
Pott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230
Cohen, Y. C., Zada, M., Wang, S.-Y., Bornstein, C., David, E., Moshe, A., Li, B., Shlomi-Loubaton, S., Gatt, M. E., Gur, C., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01232-w · Cited by 235
Self-citationSingle-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230
A shared disease-associated oligodendrocyte signature among multiple CNS pathologies
Kenigsbuch, M., Bost, P., Halevi, S., Chang, Y., Chen, S., Ma, Q., Hajbi, R., Schwikowski, B., Bodenmiller, B., Fu, H., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01104-7 · Cited by 233
Self-citationTutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218
Cell fixation and preservation for droplet-based single-cell transcriptomics
Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., et al. (2017) · BMC Biology · doi:10.1186/s12915-017-0383-5 · Cited by 218
A single-embryo, single-cell time-resolved model for mouse gastrulation
Mittnenzweig, M., Mayshar, Y., Cheng, S., Ben-Yair, R., Hadas, R., Rais, Y., Chomsky, E., Reines, N., Uzonyi, A., Lumerman, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.004 · Cited by 214
Self-citationOvercoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159
Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies
Haimon, Z., Volaski, A., Orthgiess, J., Boura-Halfon, S., Varol, D., Shemer, A., Yona, S., Zuckerman, B., David, E., Chappell-Maor, L., et al. (2018) · Nature Immunology · doi:10.1038/s41590-018-0110-6 · Cited by 209
Self-citationUsing neural networks for reducing the dimensions of single-cell RNA-Seq data
Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx681 · Cited by 197
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222
Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification
Ku, W. L., Nakamura, K., Gao, W., Cui, K., Hu, G., Tang, Q., Ni, B., & Zhao, K. (2019) · Nature Methods · doi:10.1038/s41592-019-0361-7 · Cited by 181
A Binary Cre Transgenic Approach Dissects Microglia and CNS Border-Associated Macrophages
Kim, J.-S., Kolesnikov, M., Peled-Hajaj, S., Scheyltjens, I., Xia, Y., Trzebanski, S., Haimon, Z., Shemer, A., Lubart, A., Van Hove, H., et al. (2021) · Immunity · doi:10.1016/j.immuni.2020.11.007 · Cited by 176
Self-citationXu, Y., Su, G.-H., Ma, D., Xiao, Y., Shao, Z.-M., & Jiang, Y.-Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00729-7 · Cited by 163
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158
Cadwell, C. R., Scala, F., Li, S., Livrizzi, G., Shen, S., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · Nature Protocols · doi:10.1038/nprot.2017.120 · Cited by 147
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
Giladi, A., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2017.11.011 · Cited by 168
Self-citationHeimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156
Kee, N., Volakakis, N., Kirkeby, A., Dahl, L., Storvall, H., Nolbrant, S., Lahti, L., Björklund, Å. K., Gillberg, L., Joodmardi, E., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2016.10.003 · Cited by 149
Inferring relevant cell types for complex traits by using single-cell gene expression
Calderon, D., Bhaskar, A., Knowles, D. A., Golan, D., Raj, T., Fu, A. Q., & Pritchard, J. K. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.009 · Cited by 127
SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells
Han, K. Y., Kim, K.-T., Joung, J.-G., Son, D.-S., Kim, Y. J., Jo, A., Jeon, H.-J., Moon, H.-S., Yoo, C. E., Chung, W., et al. (2018) · Genome Research · doi:10.1101/gr.223263.117 · Cited by 143
Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby076 · Cited by 138
Giladi, A., Wagner, L. K., Li, H., Dörr, D., Medaglia, C., Paul, F., Shemer, A., Jung, S., Yona, S., Mack, M., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0661-1 · Cited by 130
Self-citationDetermining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112
Efficient Generation of Transcriptomic Profiles by Random Composite Measurements
Cleary, B., Cong, L., Cheung, A., Lander, E. S., & Regev, A. (2017) · Cell · doi:10.1016/j.cell.2017.10.023 · Cited by 114
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
The role of biomarkers in personalized immunotherapy
Sankar, K., Ye, J. C., Li, Z., Zheng, L., Song, W., & Hu-Lieskovan, S. (2022) · Biomarker Research · doi:10.1186/s40364-022-00378-0 · Cited by 111
Droplet-based single cell RNAseq tools: a practical guide
Salomon, R., Kaczorowski, D., Valdes-Mora, F., Nordon, R. E., Neild, A., Farbehi, N., Bartonicek, N., & Gallego-Ortega, D. (2019) · Lab on a Chip · doi:10.1039/c8lc01239c · Cited by 109
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108
Gerlach, J. P., van Buggenum, J. A. G., Tanis, S. E. J., Hogeweg, M., Heuts, B. M. H., Muraro, M. J., Elze, L., Rivello, F., Rakszewska, A., van Oudenaarden, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37977-7 · Cited by 104
Epigenomic map of human liver reveals principles of zonated morphogenic and metabolic control
Brosch, M., Kattler, K., Herrmann, A., von Schönfels, W., Nordström, K., Seehofer, D., Damm, G., Becker, T., Zeissig, S., Nehring, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06611-5 · Cited by 98
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104
Shaashua, L., Ben-Shmuel, A., Pevsner-Fischer, M., Friedman, G., Levi-Galibov, O., Nandakumar, S., Barki, D., Nevo, R., Brown, L. E., Zhang, W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34081-3 · Cited by 79
Geometry of the Gene Expression Space of Individual Cells
Korem, Y., Szekely, P., Hart, Y., Sheftel, H., Hausser, J., Mayo, A., Rothenberg, M. E., Kalisky, T., & Alon, U. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004224 · Cited by 85
Evaluating stably expressed genes in single cells
Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D. M., Speed, T., Yang, J. Y. H., & Yang, P. (2019) · GigaScience · doi:10.1093/gigascience/giz106 · Cited by 84
Single cell transcriptomics in neuroscience: cell classification and beyond
Tasic, B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.021 · Cited by 83
bigSCale: an analytical framework for big-scale single-cell data
Iacono, G., Mereu, E., Guillaumet-Adkins, A., Corominas, R., Cuscó, I., Rodríguez-Esteban, G., Gut, M., Pérez-Jurado, L. A., Gut, I., & Heyn, H. (2018) · Genome Research · doi:10.1101/gr.230771.117 · Cited by 79
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76
Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
“can overcome these problems [9, 10], but at a cost in throughput.”
BackgroundCellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
Bernstein, M. N., Ma, Z., Gleicher, M., & Dewey, C. N. (2021) · iScience · doi:10.1016/j.isci.2020.101913 · Cited by 45
Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems
Herring, C. A., Chen, B., McKinley, E. T., & Lau, K. S. (2018) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2018.01.023 · Cited by 35
Eitan, C., Siany, A., Barkan, E., Olender, T., van Eijk, K. R., Moisse, M., Farhan, S. M. K., Danino, Y. M., Yanowski, E., Marmor-Kollet, H., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01040-6 · Cited by 38
XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples
Cheloni, S., Hillje, R., Luzi, L., Pelicci, P. G., & Gatti, E. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-00872-8 · Cited by 28
p21 facilitates chronic lung inflammation via epithelial and endothelial cells
Levi, N., Papismadov, N., Majewska, J., Roitman, L., Wigoda, N., Eilam, R., Tsoory, M., Rotkopf, R., Ovadya, Y., Akiva, H., et al. (2023) · Aging · doi:10.18632/aging.204622 · Cited by 15
Targeting individual cells by barcode in pooled sequence libraries
Ranu, N., Villani, A.-C., Hacohen, N., & Blainey, P. C. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky856 · Cited by 13
Lysosome-related organelles orchestrate guanine crystal formation in pigment cells
Gorelick-Ashkenazi, A., Barzilay, Y., Lerer-Goldshtein, T., Olender, T., Eyal, Z., Glaser, M., Broder, Y., Mishol, N., Deis, R., Kedmi, M., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2524305123 · Cited by 1
Metadata
- Authors
- Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., & Amit, I.
- Affiliations
- Weizmann Institute of Science (IL)
- Department of Immunology, Weizmann Institute, Rehovot 76100, Israel.
- Corresponding author
- Jaitin, Diego Adhemar
- Kenigsberg, Ephraim
- Tanay, Amos
- Amit, Ido
- ORCID
- Venue
- Science
- Published
- 2014-02-14
- Volume
- 343
- Issue
- 6172
- Pages
- 776-779
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4412462
- OpenAlex
- W2071010503
- Cited by
- 1,795
- References
- 24
- Citations by year
- 2026: 64 · 2025: 101 · 2024: 138 · 2023: 156 · 2022: 178 · 2021: 181 · 2020: 183 · 2019: 159 · 2018: 181 · 2017: 162 · 2016: 125 · 2015: 109 · 2014: 52
- FWCI
- 61.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (P50HG006193)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- splenic tissue, dendritic cells, cellular heterogeneity, unsupervised classification
- MeSH terms
- Animals, Dendritic Cells, Female, Hematopoiesis, Mice, Inbred C57BL, RNA, Messenger, Spleen, Transcription, Genetic, Biomarkers, Sequence Analysis, RNA, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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