Abstract
We describe microvillar cartography (MC), a method to map proteins on cellular surfaces with respect to the membrane topography. The surfaces of many cells are not smooth, but are rather covered with various protrusions such as microvilli. These protrusions may play key roles in multiple cellular functions, due to their ability to control the distribution of specific protein assemblies on the cell surface. Thus, for example, we have shown that the T-cell receptor and several of its proximal signaling proteins reside on microvilli, while others are excluded from these projections. These results have indicated that microvilli can function as key signaling hubs for the initiation of the immune response. MC has facilitated our observations of particular surface proteins and their specialized distribution on microvillar and non-microvillar compartments. MC combines membrane topography imaging, using variable-angle total internal microscopy, with stochastic localization nanoscopy, which generates deep sub-diffraction maps of protein distribution. Since the method is based on light microscopy, it avoids some of the pitfalls inherent to electron-microscopy-based techniques, such as dehydration, the need for carbon coating, and immunogold clustering, and is amenable to future developments involving, for example, live-cell imaging. This protocol details the procedures we developed for MC, which can be readily adopted to study a broad range of cell-surface molecules and dissect their distribution within distinct surface assemblies under multiple cell activation states.
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Acknowledgments
We thank Drs. Sara W. Feigelson and Ronen Alon of the Weizmann Institute of Science, Israel, and Dr. Yunmin Jung of the Institute for Basic Science Center for Nanomedicine, Seoul, Korea, for their kind involvement in this project and for their advice. G.H. is the incumbent of the Hilda Pomeraniec Memorial Professorial Chair.
Data Availability
Sample datasets for “microvillar cartography” and “co-localization probability” analysis can be downloaded from the “BioImage archive” using the links given below:
Microvillar Cartography data: https://www.ebi.ac.uk/biostudies/studies/S-BSST520 (Accession: S-BSST520)
Co-localization Probability Data: https://www.ebi.ac.uk/biostudies/studies/S-BSST521 (Accession: S-BSST521)
All other data generated during and/or analyzed during studies similar to those detailed above are available from the corresponding author upon request.
Code Availability
The custom MATLAB code described in this study can be found in the following link in the Github repository:
https://github.com/shirsendughosh/Micorvillar-Catography-and-Colocalization-Probability-Code.
The code is accompanied by operation instructions in the file “step by step guide to run analysis codes.pdf.” It can be accessed and used by readers without restriction.
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Ghosh, S., Alcover, A., Haran, G. (2023). Microvillar Cartography: A Super-Resolution Single-Molecule Imaging Method to Map the Positions of Membrane Proteins with Respect to Cellular Surface Topography. In: Baldari, C.T., Dustin, M.L. (eds) The Immune Synapse. Methods in Molecular Biology, vol 2654. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3135-5_12
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DOI: https://doi.org/10.1007/978-1-0716-3135-5_12
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