The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method
Open accessAbstract
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications.
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
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
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks
Magnuson, B., Ekim, B., & Fingar, D. C. (2012) · Biochemical Journal · doi:10.1042/bj20110892 · Cited by 1,015 · cited 2×
“The former population appears to represent quiescent cells with high TORC1 activity that are also resistant to vemurafenib-induced apoptosis 22,28 .”
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
TORC1 Suppression Predicts Responsiveness to RAF and MEK Inhibition in BRAF- Mutant Melanoma
Corcoran, R. B., Rothenberg, S. M., Hata, A. N., Faber, A. C., Piris, A., Nazarian, R. M., Brown, R. D., Godfrey, J. T., Winokur, D., Walsh, J., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005753 · Cited by 140
Fallahi‐Sichani, M., Moerke, N. J., Niepel, M., Zhang, T., Gray, N. S., & Sorger, P. K. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145877 · Cited by 99
Self-citation
Cited by
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059
Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance
Marusyk, A., Janiszewska, M., & Polyak, K. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.007 · Cited by 1,009
He, S., Bhatt, R., Brown, C., Brown, E. A., Buhr, D. L., Chantranuvatana, K., Danaher, P., Dunaway, D., Garrison, R. G., Geiss, G., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01483-z · Cited by 814
Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity
Ringel, A. E., Drijvers, J. M., Baker, G. J., Catozzi, A., García-Cañaveras, J. C., Gassaway, B. M., Miller, B. C., Juneja, V. R., Nguyen, T. H., Joshi, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.009 · Cited by 780
Self-citationhistoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
Schapiro, D., Jackson, H. W., Raghuraman, S., Fischer, J. R., Zanotelli, V. R. T., Schulz, D., Giesen, C., Catena, R., Varga, Z., & Bodenmiller, B. (2017) · Nature Methods · doi:10.1038/nmeth.4391 · Cited by 659
Metadata
- Authors
- Lin, J.-R., Fallahi-Sichani, M., & Sorger, P. K.
- Venue
- Nature Communications
- Published
- 2015-09-24
- Volume
- 6
- Issue
- 1
- Pages
- 8390-8390
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2193067168
- Cited by
- 562
- References
- 6
- FWCI
- 13.07
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. https://app.cochat.ai/papers/10.1038/ncomms9390
- The paper
- Lin, J.-R., Fallahi-Sichani, M., & Sorger, P. K. (2015). Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nature Communications. https://doi.org/10.1038/ncomms9390
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.
