The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Open accessAbstract
Cancer at single-cell resolution: Single-cell sequencing can illuminate the genetic properties of brain cancers and reveal heterogeneity within a tumor. Patel et al. examined the genome sequence of single cells isolated from brain glioblastomas. The findings revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets. The authors suggest that the variation in tumor cells reflects neural development and that such variation among cancer cells may prove to have clinical significance. Science , this issue p. 1396
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“Almost all previous work quantifying differences between single-cell datasets relies on discrete partitioning of the data before downstream analysis9–16.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [9]
In the citing paper
“Recent studies have successfully applied similar approaches to single-cell data, for example, by Patel et al. [36] to identify gene signatures and TFs that correlate with the stemness gradient in glioblastoma”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [36]
In the citing paper
“First, in contexts such as development and physiology, some facets of cellular identity are transient in time and space. Temporal processes may, for example, progress along one or more trajectories (e.g., differentiation); oscillate continuously between cellular states (e.g., the cell cycle98,99 or circadian rhythm100); or be influenced by the physical position and neighborhood of the cell71,93.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [99]
In the citing paper
“Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [99]
In the citing paper
“single-cell profiles provide a near-continuous sampling and show high conservation across cell types and species30,97,99”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [99]
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Metadata
- Authors
- Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., Louis, D. N., Rozenblatt-Rosen, O., Suvà, M. L., Regev, A., & Bernstein, B. E.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Broad Institute of Harvard and Massachusetts Institute of Techonology (MIT), Cambridge, MA 02142, USA.
- Corresponding author
- Patel, Anoop P.
- Tirosh, Itay
- Suvà, Mario L.
- Regev, Aviv
- Bernstein, Bradley E.
- ORCID
- Venue
- Science
- Published
- 2014-06-20
- Volume
- 344
- Issue
- 6190
- Pages
- 1396-1401
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4123637
- OpenAlex
- W1792532087
- Cited by
- 5,063
- References
- 47
- Citations by year
- 2026: 437 · 2025: 505 · 2024: 545 · 2023: 514 · 2022: 501 · 2021: 538 · 2020: 521 · 2019: 376 · 2018: 352 · 2017: 306 · 2016: 251 · 2015: 167 · 2014: 43
- FWCI
- 104.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Human Genome Research Institute (U54HG006991)
- National Cancer Institute (U24CA180922, P50CA165962)
- National Institute of Neurological Disorders and Stroke (R01NS032677, R25NS065743)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- glioblastoma, intratumoral heterogeneity, tumor heterogeneity, cancer stemness, transcriptional programs, oncogenic signaling, hypoxia, immune response, glioblastoma subtypes, prognostic factors, single-cell transcriptomics
- MeSH terms
- Brain Neoplasms, Glioblastoma, Humans, Prognosis, RNA, Messenger, Genetic Variation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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