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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
scRNA-seq has been used to deconvolve immune cell type heterogeneity by identifying novel distinct immune cell subsets in health and disease.
“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
The indexed text (section labeled Abstract; page unavailable) reports that single-cell RNA-seq distinguished immune-cell populations in mouse spleen and revealed new transcriptional subpopulations after LPS exposure. But LPS was used to mimic infection; the paper does not report studying disease, and it cautions that transcriptional subpopulations need not be distinct, committed cell subtypes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSingle-cell RNA sequencing has emerged as a powerful method for unbiased discovery of cell types and states based on gene activity.
“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
The level of spurious UMIs in the data was estimated using statistics on empty MARS-seq wells as previously described.
“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
The indexed text describes MARS-Seq’s molecular barcoding, molecule counting, and filtering in the section labeled Abstract, but does not describe estimating spurious UMIs from empty wells. Searches for “empty MARS-seq wells spurious UMIs” and “empty wells background noise molecular barcodes false molecules” found no passage addressing that procedure. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBy 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 levels.
“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]
As few as 20,000 reads have been used to differentiate cell types in splenic tissue.
“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]
Imperfections in capture, conversion, and amplification of RNA in individual cells lead to both false negatives and false positives for individual transcripts, which may confound understanding of biological variation.
“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]
Pre-sorting cells into wells can avoid these problems but reduces throughput.
“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
Many scRNA-seq approaches introduced over the last ten years are now key for measuring cell-to-cell variation and its effects on function across populations.
“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]
Other protocols have sacrificed full-length coverage to sequence part of the cDNA primer, enabling early cell-specific barcoding and increasing scRNA-seq library generation throughput by one to three orders of magnitude.
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · 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
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 · 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
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 · 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
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 · see citing works
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696 · see citing works
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 693 · see citing works
Unravelling mononuclear phagocyte heterogeneity
Geissmann, F., Gordon, S., Hume, D. A., Mowat, A. M., & Randolph, G. J. (2010) · Nature reviews. Immunology · doi:10.1038/nri2784 · Cited by 529 · see citing works
Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses
Amit, I., Garber, M., Chevrier, N., Leite, A. P., Donner, Y., Eisenhaure, T., Guttman, M., Grenier, J. K., Li, W., Zuk, O., et al. (2009) · Science · doi:10.1126/science.1179050 · Cited by 488 · see citing works
Self-citationSasagawa, 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
The External RNA Controls Consortium: a progress report
The External RNA Controls Consortium (2005) · Nature Methods · doi:10.1038/nmeth1005-731 · Cited by 373 · see citing works
Dendritic Cell and Macrophage Heterogeneity In Vivo
Hashimoto, D., Miller, J. C., & Mérad, M. (2011) · Immunity · doi:10.1016/j.immuni.2011.09.007 · Cited by 366 · see citing works
Development and applications of single-cell transcriptome analysis
Tang, F., Lao, K., & Surani, M. A. (2011) · Nature Methods · doi:10.1038/nmeth.1557 · Cited by 337 · see citing works
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 · see citing works
A monoclonal antibody specific for mouse dendritic cells.
Nussenzweig, M. C., Steinman, R. M., Witmer, M. D., & Gutchinov, B. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.1.161 · Cited by 250 · see citing works
Bar‐On, L., Birnberg, T., Lewis, K. L. T., Edelson, B. T., Bruder, D., Hildner, K. M., Buer, J., MURPHY, K. M. M., Reizis, B., & Jung, S. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001562107 · Cited by 156 · see citing works
Self-citationHume, D. A. (2011) · Journal of Leukocyte Biology · doi:10.1189/jlb.0810472 · Cited by 154 · see citing works
Microbial environments confound antibiotic efficacy
Lee, H. H., & Collins, J. J. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.740 · Cited by 143 · see citing works
Maintaining system homeostasis: the third law of Newtonian immunology
Germain, R. N. (2012) · Nature Immunology · doi:10.1038/ni.2404 · Cited by 88 · see citing works
Bradford, B. M., Sester, D. P., Hume, D. A., & Mabbott, N. A. (2011) · Immunobiology · doi:10.1016/j.imbio.2011.08.006 · Cited by 47 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [10]Bar-On L Proc Natl Acad Sci U S A 107 14745 14750 2010 20679228 PMC2930429
Cited by
Sorted by citations
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · 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
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
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493 · see citing works
Self-citationDroplet 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
Host microbiota constantly control maturation and function of microglia in the CNS
Erny, D., Hrabě de Angelis, A. L., Jaitin, D., Wieghofer, P., Staszewski, O., David, E., Keren-Shaul, H., Mahlakoiv, T., Jakobshagen, K., Buch, T., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4030 · Cited by 3,569 · see citing works
Self-citationCell 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
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
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 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationBatch 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
Tissue-Resident Macrophage Enhancer Landscapes Are Shaped by the Local Microenvironment
Lavin, Y., Winter, D., Blecher-Gonen, R., David, E., Keren-Shaul, H., Merad, M., Jung, S., & Amit, I. (2014) · Cell · doi:10.1016/j.cell.2014.11.018 · Cited by 2,167 · see citing works
Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
Tissue-Resident Macrophage Ontogeny and Homeostasis
Ginhoux, F., & Guilliams, M. (2016) · Immunity · doi:10.1016/j.immuni.2016.02.024 · Cited by 1,884 · see citing works
Monocytes and macrophages: developmental pathways and tissue homeostasis
Ginhoux, F., & Jung, S. (2014) · Nature Reviews Immunology · doi:10.1038/nri3671 · Cited by 1,840 · see citing works
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680 · 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
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 · 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
Single-cell transcriptional diversity is a hallmark of developmental potential
Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571 · see citing works
CellMarker: a manually curated resource of cell markers in human and mouse
Zhang, X., Lan, Y., Xu, J., Quan, F., Zhao, E., Deng, C., Luo, T., Xu, L., Liao, G., Yan, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky900 · Cited by 1,524 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
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 · see citing works
Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner
Jaitin, D. A., Adlung, L., Thaiss, C. A., Weiner, A., Li, B., Descamps, H., Lundgren, P., Bleriot, C., Liu, Z., Deczkowska, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.054 · Cited by 1,377 · see citing works
Self-citationNASH limits anti-tumour surveillance in immunotherapy-treated HCC
Pfister, D., Núñez, N. G., Pinyol, R., Govaere, O., Pinter, M., Szydlowska, M., Gupta, R., Qiu, M., Deczkowska, A., Weiner, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03362-0 · Cited by 1,294 · 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
Microglia development follows a stepwise program to regulate brain homeostasis
Matcovitch-Natan, O., Winter, D. R., Giladi, A., Vargas Aguilar, S., Spinrad, A., Sarrazin, S., Ben-Yehuda, H., David, E., Zelada González, F., Perrin, P., et al. (2016) · Science · doi:10.1126/science.aad8670 · Cited by 1,180 · see citing works
Self-citationSingle-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 · see citing works
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 · 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
Li, H., van der Leun, A. M., Yofe, I., Lubling, Y., Gelbard-Solodkin, D., van Akkooi, A. C. J., van den Braber, M., Rozeman, E. A., Haanen, J. B. A. G., Blank, C. U., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.043 · Cited by 1,101 · see citing works
Self-citationTranscriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Self-citationA 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 · see citing works
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · 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
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
The role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · 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
Human haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · see citing works
Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection
Thaiss, C. A., Levy, M., Grosheva, I., Zheng, D., Soffer, E., Blacher, E., Braverman, S., Tengeler, A. C., Barak, O., Elazar, M., et al. (2018) · Science · doi:10.1126/science.aar3318 · Cited by 942 · 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
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 · see citing works
Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations
Thaiss, C. A., Levy, M., Korem, T., Dohnalová, L., Shapiro, H., Jaitin, D. A., David, E., Winter, D. R., Gury-BenAri, M., Tatirovsky, E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.003 · Cited by 870 · see citing works
Self-citationSpecification of tissue-resident macrophages during organogenesis
Mass, E., Ballesteros, I., Farlik, M., Halbritter, F., Günther, P., Crozet, L., Jacome-Galarza, C. E., Händler, K., Klughammer, J., Kobayashi, Y., et al. (2016) · Science · doi:10.1126/science.aaf4238 · Cited by 891 · see citing works
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 · see citing works
Chromatin state dynamics during blood formation
Lara-Astiaso, D., Weiner, A., Lorenzo-Vivas, E., Zaretsky, I., Jaitin, D. A., David, E., Keren-Shaul, H., Mildner, A., Winter, D., Jung, S., et al. (2014) · Science · doi:10.1126/science.1256271 · Cited by 776 · see citing works
Self-citationDissecting 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
Self-citationG&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 · see citing works
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · see citing works
Self-citationA single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works
Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647 · see citing works
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · see citing works
Regulation of macrophage development and function in peripheral tissues
Lavin, Y., Mortha, A., Rahman, A., & Merad, M. (2015) · Nature Reviews Immunology · doi:10.1038/nri3920 · Cited by 680 · see citing works
De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
Grün, D., Muraro, M. J., Boisset, J.-C., Wiebrands, K., Lyubimova, A., Dharmadhikari, G., van den Born, M., van Es, J., Jansen, E., Clevers, H., et al. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.05.010 · Cited by 586 · see citing works
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3445 · Cited by 581 · see citing works
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
Tumour heterogeneity and metastasis at single-cell resolution
Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N., & Werb, Z. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0236-7 · Cited by 606 · see citing works
Aging-induced type I interferon response at the choroid plexus negatively affects brain function
Baruch, K., Deczkowska, A., David, E., Castellano, J. M., Miller, O., Kertser, A., Berkutzki, T., Barnett-Itzhaki, Z., Bezalel, D., Wyss-Coray, T., et al. (2014) · Science · doi:10.1126/science.1252945 · Cited by 615 · see citing works
Self-citationGury-BenAri, M., Thaiss, C. A., Serafini, N., Winter, D. R., Giladi, A., Lara-Astiaso, D., Levy, M., Salame, T. M., Weiner, A., David, E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.043 · Cited by 587 · see citing works
Self-citationImpaired immune surveillance accelerates accumulation of senescent cells and aging
Ovadya, Y., Landsberger, T., Leins, H., Vadai, E., Gal, H., Biran, A., Yosef, R., Sagiv, A., Agrawal, A., Shapira, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07825-3 · Cited by 578 · see citing works
Self-citationDesign and Analysis of Single-Cell Sequencing Experiments
Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536 · see citing works
Zhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., Shukla, A. K., Petyuk, V. A., Campbell-Thompson, M., Mathews, C. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03367-w · Cited by 587 · 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
Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
Brunner, A., Thielert, M., Vasilopoulou, C., Ammar, C., Coscia, F., Mund, A., Hoerning, O. B., Bache, N., Apalategui, A., Lubeck, M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110798 · Cited by 544 · see citing works
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients
Bost, P., Giladi, A., Liu, Y., Bendjelal, Y., Xu, G., David, E., Blecher-Gonen, R., Cohen, M., Medaglia, C., Li, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.006 · Cited by 471 · see citing works
Self-citationIntegrated 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 · see citing works
Katzenelenbogen, Y., Sheban, F., Yalin, A., Yofe, I., Svetlichnyy, D., Jaitin, D. A., Bornstein, C., Moshe, A., Keren-Shaul, H., Cohen, M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.032 · Cited by 498 · see citing works
Self-citationSingle-cell analysis tools for drug discovery and development
Heath, J. R., Ribas, A., & Mischel, P. S. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2015.16 · Cited by 473 · see citing works
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439 · see citing works
Cellular Taxonomy of the Mouse Striatum as Revealed by Single-Cell RNA-Seq
Gokce, O., Stanley, G. M., Treutlein, B., Neff, N. F., Camp, J. G., Malenka, R. C., Rothwell, P. E., Fuccillo, M. V., Südhof, T. C., & Quake, S. R. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.06.059 · Cited by 430 · see citing works
Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474 · see citing works
Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program
Geirsdottir, L., David, E., Keren-Shaul, H., Weiner, A., Bohlen, S. C., Neuber, J., Balic, A., Giladi, A., Sheban, F., Dutertre, C.-A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.010 · Cited by 461 · see citing works
Self-citationHigh-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
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
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 · see citing works
Cell type transcriptome atlas for the planarian <i>Schmidtea mediterranea</i>
Fincher, C. T., Wurtzel, O., de Hoog, T., Kravarik, K. M., & Reddien, P. W. (2018) · Science · doi:10.1126/science.aaq1736 · Cited by 436 · see citing works
Magen, A., Hamon, P., Fiaschi, N., Soong, B. Y., Park, M. D., Mattiuz, R., Humblin, E., Troncoso, L., D’souza, D., Dawson, T., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02345-0 · Cited by 429 · see citing works
Self-citationBaruch, K., Deczkowska, A., Rosenzweig, N., Tsitsou-Kampeli, A., Sharif, A. M., Matcovitch-Natan, O., Kertser, A., David, E., Amit, I., & Schwartz, M. (2016) · Nature Medicine · doi:10.1038/nm.4022 · Cited by 425 · see citing works
Self-citationSathyamurthy, A., Johnson, K. R., Matson, K. J. E., Dobrott, C. I., Li, L., Ryba, A. R., Bergman, T. B., Kelly, M. C., Kelley, M. W., & Levine, A. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.003 · Cited by 370 · see citing works
Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR
Zemmour, D., Zilionis, R., Kiner, E., Klein, A. M., Mathis, D., & Benoist, C. (2018) · Nature Immunology · doi:10.1038/s41590-018-0051-0 · Cited by 384 · see citing works
Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells
Halpern, K. B., Shenhav, R., Massalha, H., Toth, B., Egozi, A., Massasa, E. E., Medgalia, C., David, E., Giladi, A., Moor, A. E., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4231 · Cited by 368 · see citing works
Self-citationSingle-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · 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
MultiVI: deep generative model for the integration of multi-modal data
Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · doi:10.1101/2021.08.20.457057 · Cited by 385 · see citing works
Giladi, A., Paul, F., Herzog, Y., Lubling, Y., Weiner, A., Yofe, I., Jaitin, D., Cabezas-Wallscheid, N., Dress, R., Ginhoux, F., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0121-4 · Cited by 366 · see citing works
Self-citationThe 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 · see citing works
Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells
Gupta, I., Collier, P. G., Haase, B., Mahfouz, A., Joglekar, A., Floyd, T., Koopmans, F., Barres, B., Smit, A. B., Sloan, S. A., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4259 · Cited by 343 · see citing works
Single-cell mapping of the thymic stroma identifies IL-25-producing tuft epithelial cells
Bornstein, C., Nevo, S., Giladi, A., Kadouri, N., Pouzolles, M., Gerbe, F., David, E., Machado, A., Chuprin, A., Tóth, B., et al. (2018) · Nature · doi:10.1038/s41586-018-0346-1 · Cited by 317 · see citing works
Self-citationInterpretable 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
- 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 · 2013: 3 · 2012: 1
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
