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.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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
The check wasn’t confident either way, so it gives no verdict — never disputed.
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
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 554
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
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
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 378
The External RNA Controls Consortium: a progress report
The External RNA Controls Consortium (2005) · Nature Methods · doi:10.1038/nmeth1005-731 · Cited by 373
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 342
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
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
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
Self-citationHume, D. A. (2010) · Journal of Leukocyte Biology · doi:10.1189/jlb.0810472 · Cited by 144
Microbial environments confound antibiotic efficacy
Lee, H. H., & Collins, J. J. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.740 · Cited by 143
Maintaining system homeostasis: the third law of Newtonian immunology
Germain, R. N. (2012) · Nature Immunology · doi:10.1038/ni.2404 · Cited by 88
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
Flagged first, then by citations
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
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
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
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
Self-citation
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 ›
Are you an author, or do you see a problem with this page? Request a review
