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Introduction:

Clathrin coated pits (CCPs) are spherical, cage-like structures located on the plasma membrane that are responsible for receptor-mediated endocytosis. CCPs bud off the cell membrane with their receptor cargo in a process called clathrin-mediated endocytosis (CME), playing a key role in intracellular transport between the golgi networks and endosomes [Almers, 2002].

CCPs are formed by the assembly of major coat proteins, composed of three clathrin-heavy chains and tightly associated clathrin-light chains. In addition to the major coat proteins, CCP activity relies on the association with numerous accessory proteins that promote and regulate endocytosis e.g. by acting as scaffolds or recruiting cargo [Schmid, 2018]. To better understand the process of CME, it is important to be able to visualize the structure of pits and assess their molecular interactions at all stages of CCP formation. However, as the pits are typically between 150-200 nm in diameter, they are below the resolvable limit of conventional light microscopy and while the structure of CCPs can be clearly resolved by electron microscopy (EM), it is technically challenging to assess CCP interactions with accessory proteins through this method [Anderson, 1989].

Super-resolution techniques such as single-molecule localization microscopy (SMLM) have been employed to image CCPs and their structures using fluorescence based methods.[Zhuang, 2008] These techniques have advantages over electron microscopy, with the ability to image multiple markers simultaneously and assess protein spatial distributions. The array of SMLM techniques can break the diffraction limit associated with standard light microscopy to image CCPs with greater than 20 nm resolution. The small size of these CCPs means that when imaged by conventional light microscopy, they will appear as diffraction limited spots making it impossible to resolve their structures (Figure 1). Therefore in order for us to gain a better understanding of how the structure and assembly of these CCPs help to facilitate endocytosis, we need powerful super-resolution techniques.

Figure 1. Single CCP within fixed COS-7 cells was imaged through standard widefield microscopy in TIRF mode (left) and dSTORM (right). The CCP was labelled with a rabbit anti-clathrin heavy chain primary antibody and an AlexaFluor™ 647 anti-rabbit secondary antibody.

Here, we have imaged CCPs using the ONI’s Nanoimager, a desktop-compatible, single-molecule imaging system specialized in SMLM techniques. With the array of imaging modalities available on the microscope, we were able to apply both dSTORM and DNA-PAINT to overcome the diffraction-limit and demonstrate the power of these single-molecule localization techniques in visualizing and resolving CCPs. With integrated software tools, it was then possible to perform cluster analysis of the super-resolution images and characterise individual CCPs based on parameters such as size and shape.

Results:

DNA-PAINT is an SMLM method for overcoming the diffraction limit using a simple reagent based approach. In this example, CCPs were labelled with antibodies against clathrin heavy chain [Leterrier, 2020], but then detected with commercially available secondary antibodies conjugated with short single-stranded DNA “docking” strands (Massive-sdAb 2-Plex kit by Massive Photonics). Complementary DNA “imager” strands containing fluorescent dyes were added which transiently bind to their complementary DNA “docking” strand on the antibody target. This binding and unbinding of fluorescently labeled DNA allows for the position of individual fluorescent molecules to be localized within each diffraction-limited fluorescent spot over a large number of frames to reconstruct the final super-resolution image point-by-point (Figure 2, A). The powerful resolutions achieved by these techniques means that single CCPs can be analysed with sub-20 nm localization precision (Figure 2, C). DNA-PAINT is advantageous in comparison to traditional SMLM techniques because it allows for multiple targets to be imaged with different indexing DNA strands and can be used with microfluidics for multiplexed imaging of a sample that has only been stained once for as many targets as the user desires and has available antibodies. Additionally, the imaging buffer has advantages over dSTORM buffers in having a simple non-enzymatic, non-toxic composition that is room temperature stable and can be imaged extensively without exhaustion.

Figure 2: Clathrin coated pits in fixed COS-7 cells labelled with a rabbit anti-clathrin heavy chain primary antibody and detected with the Massive Photonics “massive sdAb-2-plex” kit for DNA-PAINT imaging.

Clustering techniques can be applied to single-molecule localization images to extract quantitative information about the sample. Clustering analysis works by identifying the localizations from the DNA-PAINT image which correspond to the clathrin heavy chain signal on the CCPs surface. All localizations within a defined radius are grouped into a dense circular cluster representing a single CCPs (Figure 3, A). This is then applied to the whole DNA-PAINT image, identifying thousands of CCPs clusters within the cell (Figure 3, B). Once these clusters have been identified, they can be constrained on parameters that represent real CCPs in a biological sample such as size, length and circularity. This provides additional confidence that only real CCPs are being analysed over any aggregates or background within the sample. After clustering has been applied to the image, quantitative information can be obtained on the size range of CCPs within the sample (Figure 3, C). The mean size of CCP cluster in these data of 180nm +/- 30nm is comparable to previously published dSTORM analysis of CCPs, which itself was benchmarked to data collected using electron microscopy (180nm +/- 40nm, Huang et al). This quantitative analysis is important for understanding the different stages of CCP formation within cells (for example by constraining based on size or shape features of the clusters) or population dynamics of CCPs (for example, quantifying the number of CCP clusters of various sub-categories in experimental versus control conditions), which will give further insight into their role in receptor-mediated endocytosis.

Figure 3: Clustering analysis was applied to the DNA-PAINT images to extract sizing information of the CCPs. Single clusters representing single CCPs showing the ring-like structures (A) and All CCP clusters detected with fixed COS7 cells (B) and histogram plot showing size ranges of CCPs within the cells (C).

Summary:

These experiments demonstrate the power of SMLM in resolving single CCPs and characterizing their molecular structures and sizes, something which is not possible through standard light microscopy techniques. DNA-PAINT methods in combination with SMLM allows for super-resolution images to be acquired with sub-20 nm localization precision. This in combination with its multiplexing potential means that DNA-PAINT methods can be used to fully automate SMLM imaging workflows.

The Nanoimager offers a user-friendly, desktop-compatible single-molecule imaging solution for the precise and detailed characterization of clathrin coated pits and other subcellular structures of interest via a range of SMLM techniques such as PALM, dSTORM and DNA-PAINT. In-built analysis tools with customizable workflows allow for full characterization of the structure and number of CCPs, which is important for understanding their functions within cells.

 

References
1. Almers W. Imaging actin and dynamin recruitment during invagination of single clathrin-coated pits. Nat Cell Biol. 2002 Sep;4(9):691-8.
2. Schmid S. Regulation of Clathrin-Mediated Endocytosis. Annu Rev Biochem. 2018 Jun 20;87:871-896.
3. Anderson R G. Hypertonic media inhibit receptor-mediated endocytosis by blocking clathrin-coated pit formation. J Cell Biol. 1989 Feb; 108(2):389-400.
4. Zhuang X. Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy. Science. 2008 Feb; Vol. 319, 5864:810-813
5. Leterrier C. About samples, giving examples: Optimized Single Molecule Localization Microscopy. Methods. 2020 Mar; 174:100-114

 

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