Statistical Analysis of Scanning Fluorescence Correlation Spectroscopy Data Differentiates Free from Hindered Diffusion
Source abstract
Abstract
Cells rely on versatile diffusion dynamics in their plasma membrane. Quantification of this often heterogeneous diffusion is essential to the understanding of cell regulation and function. Yet such measurements remain a major challenge in cell biology, usually due to low sampling throughput, a necessity for dedicated equipment, sophisticated fluorescent label strategies, and limited sensitivity. Here, we introduce a robust, broadly applicable statistical analysis pipeline for large scanning fluorescence correlation spectroscopy data sets, which uncovers the nanoscale heterogeneity of the plasma membrane in living cells by differentiating free from hindered diffusion modes of fluorescent lipid and protein analogues.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Lipid Membrane Structure and Behavior · Advanced Fluorescence Microscopy Techniques · Spectroscopy and Quantum Chemical Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Applied Optics, Friedrich-Schiller-University and Leibniz Institute of Photonic Technology, Helmholtzweg 4, 07743 Jena, Germany
Kennedy Institute for Rheumatology, University of Oxford, Roosevelt Drive, Oxford OX3 7LF, United Kingdom
Funding
John Fell Fund, University of Oxford · Engineering and Physical Sciences Research Council · Medical Research Council · University of Oxford · Wellcome Trust · Wolfson Foundation · Biotechnology and Biological Sciences Research Council
References (38)
Show source-provided references
1. 10.1126/science.83127910.1126/science.831279 ↗
2. 10.1002/(SICI)1097-0320(19990701)36:3<176::AID-CYTO5>3.0.CO;2-F10.1002/(SICI)1097-0320(19990701)36:3<176::AID-CYTO5>3.0.CO;2-F ↗
3. 10.1146/annurev.biophys.34.040204.14463710.1146/annurev.biophys.34.040204.144637 ↗
4. 10.1073/pnas.93.7.292610.1073/pnas.93.7.2926 ↗
5. 10.1083/jcb.20020205010.1083/jcb.200202050 ↗
6. 10.1126/science.55665310.1126/science.556653 ↗
7. 10.1038/nprot.2015.04210.1038/nprot.2015.042 ↗
8. 10.1529/biophysj.105.06795910.1529/biophysj.105.067959 ↗
9. 10.1529/biophysj.104.05487410.1529/biophysj.104.054874 ↗
10. 10.1038/nature0759610.1038/nature07596 ↗
11. 10.1371/journal.pone.012177710.1371/journal.pone.0121777 ↗
12. 10.1016/S0006-3495(93)81173-110.1016/S0006-3495(93)81173-1 ↗
13. 10.1016/j.bpj.2009.08.02510.1016/j.bpj.2009.08.025 ↗
14. 10.1073/pnas.122209711010.1073/pnas.1222097110 ↗
15. 10.1016/j.bpj.2015.02.00310.1016/j.bpj.2015.02.003 ↗
16. 10.1021/ac303485t10.1021/ac303485t ↗
17. 10.1529/biophysj.103.03648310.1529/biophysj.103.036483 ↗
18. 10.1016/j.bpj.2008.12.388810.1016/j.bpj.2008.12.3888 ↗
19. 10.1006/jmbi.2000.369210.1006/jmbi.2000.3692 ↗
20. 10.1016/j.ymeth.2017.09.01010.1016/j.ymeth.2017.09.010 ↗
21. 10.1529/biophysj.106.08229710.1529/biophysj.106.082297 ↗
22. 10.1038/ncomms549410.1038/ncomms5494 ↗
23. 10.1091/mbc.e17-07-045910.1091/mbc.e17-07-0459 ↗
24. 10.1038/srep1014910.1038/srep10149 ↗
25. 10.1146/annurev-physchem-032210-10342410.1146/annurev-physchem-032210-103424 ↗
26. 10.1038/ncomms641210.1038/ncomms6412 ↗
27. 10.1038/ncomms309310.1038/ncomms3093 ↗
28. 10.1016/j.ymeth.2017.12.00510.1016/j.ymeth.2017.12.005 ↗
29. 10.1126/sciadv.160303210.1126/sciadv.1603032 ↗
30. 10.1016/j.cell.2016.07.00310.1016/j.cell.2016.07.003 ↗
31. 10.1091/mbc.e16-07-053610.1091/mbc.e16-07-0536 ↗
32. 10.1038/srep1145410.1038/srep11454 ↗
33. 10.1177/004912410426864410.1177/0049124104268644 ↗
34. 10.1088/1361-6463/aa519e10.1088/1361-6463/aa519e ↗
35. 10.1214/aos/117634413610.1214/aos/1176344136 ↗
36. 10.1016/j.bpj.2011.09.00610.1016/j.bpj.2011.09.006 ↗
37. 10.1021/acs.nanolett.7b0297310.1021/acs.nanolett.7b02973 ↗
38. Waithe, D. GitHub Nanosimpy Repository, https://github.com/dwaithe/nanosimpy (accessed July 19, 2018).