The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells
Open accessAbstract
We describe a microchip designed to quantify the levels of a dozen cytoplasmic and membrane proteins from single cells. We use the platform to assess protein–protein interactions associated with the EGF-receptor-mediated PI3K signaling pathway. Single-cell sensitivity is achieved by isolating a defined number of cells ( n = 0–5) in 2 nL volume chambers, each of which is patterned with two copies of a miniature antibody array. The cells are lysed on-chip, and the levels of released proteins are assayed using the antibody arrays. We investigate three isogenic cell lines representing the cancer glioblastoma multiforme, at the basal level, under EGF stimulation, and under erlotinib inhibition plus EGF stimulation. The measured protein abundances are consistent with previous work, and single-cell analysis uniquely reveals single-cell heterogeneity, and different types and strengths of protein–protein interactions. This platform helps provide a comprehensive picture of altered signal transduction networks in tumor cells and provides insight into the effect of targeted therapies on protein signaling networks.
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.
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
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Molecular determinants of the response of glioblastomas to EGFR kinase inhibitors
Mellinghoff, I. K., Wang, M. Y., Vivanco, I., Haas-Kogan, D. A., Zhu, S., Dia, E. Q., Lu, K. V., Yoshimoto, K., Huang, J. H. Y., Chute, D. J., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa051918 · Cited by 1,442
Self-citationMa, C., Fan, R., Ahmad, H., Shi, Q., Comin-Anduix, B., Chodon, T., Koya, R. C., Liu, C.-C., Kwong, G. A., Radu, C. G., et al. (2011) · Nature Medicine · doi:10.1038/nm.2375 · Cited by 441 · cited 1×
“Background levels frommicrochambers containing zero cells, correlations of protein signal strengths with numbers of cells (SI Appendix, Methods III), and comparison of SCBC assays with traditional (Western blotting) assays, as well as with flow cytometry (19) have all been measured to further validate the SCBC assays.”
MethodSelf-citationShe, Q.-B., Halilovic, E., Ye, Q., Zhen, W., Shirasawa, S., Sasazuki, T., Solit, D. B., & Rosen, N. (2010) · Cancer Cell · doi:10.1016/j.ccr.2010.05.023 · Cited by 400 · cited 15×
“This preferential activation of MAPK signaling pathways in response to EGF activation in GBM cells containing PTEN was validated by immunoblot analysis (Fig.”
BackgroundInfluentialTargeting RAS and PI3K in lung cancer
Downward, J. (2008) · Nature Medicine · doi:10.1038/nm1208-1315 · Cited by 80
Cited by
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177
Hood, L., & Flores, M. (2012) · New Biotechnology · doi:10.1016/j.nbt.2012.03.004 · Cited by 648
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
Self-citationNanoparticles for combination drug therapy
Ma, L., Kohli, M., & Smith, A. (2013) · ACS Nano · doi:10.1021/nn405674m · Cited by 367
Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B., & Livak, K. J. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00449-x · Cited by 282
Metadata
- Authors
- Shi, Q., Qin, L., Wei, W., Geng, F., Fan, R., Shik Shin, Y., Guo, D., Hood, L., Mischel, P. S., & Heath, J. R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-12-27
- Volume
- 109
- Issue
- 2
- Pages
- 419-424
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2061078277
- Cited by
- 318
- References
- 5
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
