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
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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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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
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