The DOI matches its Crossref record.
Probing protein heterogeneity in the plasma membrane using PALM and pair correlation analysis
Open accessAbstract
Photoactivated localization microscopy (PALM) is a powerful approach for investigating protein organization, yet tools for quantitative, spatial analysis of PALM datasets are largely missing. Combining pair-correlation analysis with PALM (PC-PALM), we provide a method to analyze complex patterns of protein organization across the plasma membrane without determination of absolute protein numbers. The approach uses an algorithm to distinguish a single protein with multiple appearances from clusters of proteins. This enables quantification of different parameters of spatial organization, including the presence of protein clusters, their size, density and abundance in the plasma membrane. Using this method, we demonstrate distinct nanoscale organization of plasma-membrane proteins with different membrane anchoring and lipid partitioning characteristics in COS-7 cells, and show dramatic changes in glycosylphosphatidylinositol (GPI)-anchored protein arrangement under varying perturbations. PC-PALM is thus an effective tool with broad applicability for analysis of protein heterogeneity and function, adaptable to other single-molecule strategies.
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
Cited by
The mystery of membrane organization: composition, regulation and roles of lipid rafts
Sezgin, E., Levental, I., Mayor, S., & Eggeling, C. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.16 · Cited by 2,124
Super-resolution microscopy with DNA-PAINT
Schnitzbauer, J., Strauss, M. T., Schlichthaerle, T., Schueder, F., & Jungmann, R. (2017) · Nature Protocols · doi:10.1038/nprot.2017.024 · Cited by 1,047
Fluorescence nanoscopy in cell biology
Sahl, S. J., Hell, S. W., & Jakobs, S. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.71 · Cited by 990
Dynamic regulation of transcriptional states by chromatin and transcription factors
Voss, T. C., & Hager, G. L. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3623 · Cited by 504
SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
Levet, F., Hosy, E., Kechkar, A., Butler, C., Beghin, A., Choquet, D., & Sibarita, J.-B. (2015) · Nature Methods · doi:10.1038/nmeth.3579 · Cited by 502
Pessina, F., Giavazzi, F., Yin, Y., Gioia, U., Vitelli, V., Galbiati, A., Barozzi, S., Garre, M., Oldani, A., Flaus, A., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0392-4 · Cited by 360
Veatch, S. L., Machta, B. B., Shelby, S. A., Chiang, E. N., Holowka, D. A., & Baird, B. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031457 · Cited by 290
Self-citationNanoclustering as a dominant feature of plasma membrane organization
Garcia-Parajo, M. F., Cambi, A., Torreno-Pina, J. A., Thompson, N., & Jacobson, K. (2014) · Journal of Cell Science · doi:10.1242/jcs.146340 · Cited by 284
Synaptic weight set by Munc13-1 supramolecular assemblies
Sakamoto, H., Ariyoshi, T., Kimpara, N., Sugao, K., Taiko, I., Takikawa, K., Asanuma, D., Namiki, S., & Hirose, K. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0041-9 · Cited by 235
Sengupta, P., Seo, A. Y., Pasolli, H. A., Song, Y. E., Johnson, M. C., & Lippincott-Schwartz, J. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0300-y · Cited by 118
Self-citationGudheti, M. V., Curthoys, N. M., Gould, T. J., Kim, D., Gunewardene, M. S., Gabor, K. A., Gosse, J. A., Kim, C. H., Zimmerberg, J., & Hess, S. T. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.03.054 · Cited by 106
Roy, N. H., Chan, J., Lambelé, M., & Thali, M. (2013) · Journal of Virology · doi:10.1128/jvi.00790-13 · Cited by 89
Petersen, E. N., Pavel, M. A., Wang, H., & Hansen, S. B. (2020) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2019.183091 · Cited by 36
‘Assembly and analysis of cell-scale membrane envelopes’
Vermaas, J. V., Mayne, C. G., Shinn, E., & Tajkhorshid, E. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01050 · Cited by 30
Hoffman, H. K., Fernandez, M. V., Groves, N. S., Freed, E. O., & van Engelenburg, S. B. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.009372 · Cited by 28
Metadata
- Authors
- Sengupta, P., Jovanovic-Talisman, T., Skoko, D., Renz, M., Veatch, S. L., & Lippincott-Schwartz, J.
- Venue
- Nature Methods
- Published
- 2011-09-18
- Volume
- 8
- Issue
- 11
- Pages
- 969-975
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2043891370
- Cited by
- 588
- References
- 0
- FWCI
- 27.77
- 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.
