Apr 17, 2026
Localization of pSYN aggregates to either axons or dendrites
- Andrew D Sauerbeck1,
- Terrance T Kummer1
- 1Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA.
Protocol Citation: Andrew D Sauerbeck, Terrance T Kummer 2026. Localization of pSYN aggregates to either axons or dendrites. protocols.io https://dx.doi.org/10.17504/protocols.io.dm6gp7kjpgzp/v1
License: This is an open access  protocol distributed under the terms of the  Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 17, 2026
Last Modified: April 17, 2026
Protocol Integer ID: 315213
Keywords: ASAPCRN, synuclein, zeiss airyscan microscope, useful insight into biological process, axon, phosphorylated alpha, disease pathology, dendrite, underlying disease pathology, output images for visualization, localization of psyn, output image, biological process
Funders Acknowledgements:
Aligning Science Across Parkinson's
Grant ID: ASAP-020616
Abstract
Localization of phosphorylated alpha-synuclein aggregates to either an axon or dendrite provides useful insight into biological processes underlying disease pathology. This protocol provides for the analysis of super-resolution images acquired on a Zeiss Airyscan microscope. Endpoints provided by this analysis workflow include identifying which aggregates are confidently identified as axonal or dendritic and output images for visualization and quality control. The code described in this protocol can be obtained from https://github.com/KummerLab/SEQUIN .
Before start
- Neurite mask intensity cutoff**: Must be manually calculated before masking. Recommended value is 95th percentile.
- Channel Alignment of raw images**: User must provide values specific to the system and objective used in the experiment.
-Binary masks of images to colocalize**.Must be manually generated before performing analysis.
The steps below reference the code: neurite_axon_dendrite_colocalization_analysis_v1
To process Airyscan images and perform colocalization, the user must first load all required python packages defined in the code.
After the required packages are loaded, the user must next define the input parameters for the given experiment. These parameters are specific to a given experiment, may require adjustment for experimental conditions, and must be confirmed appropriate by the experimenter.
The user must provide the location of the image to be processed, the folder for saving output images, the folder for saving copies of preprocessed images, and the naming suffix to use when saving images from this code.
The user must have already generated binarized masks of the images to be processed. Since they can vary significantly by experimental setup, they must be generated manually and confirmed accurate before using this code.
For tilescan images, it is recommended to use a fixed intensity cutoff to consistently identify areas of positive aggregate labeling across all tiles.
A critical step of colocalization is careful measurement and correction for chromatic aberration. This is done through the staining, imaging, and analysis as Channel Alignment images. It is recommended to use a subset of the brightest puncta in the images for this measurement, with the top 5% being a good reference point. The user must specify which channel in the original raw image to use as the reference and which channel(s) to align to this image. If doing three channel imaging, it is recommended to use the fluorophore with the most central wavelength as the reference to reduce the effect of image cropping that is applied after aligning the channels. The user must provide values specific to the system and objective used in the experiment.
The user must set the parameters to use for colocalization analysis. The parameter 'mask_type' can be set to 'positive', which will use the entire raw binarized mask, or 'negative' which will only use areas of the binarized mask that are not also occupied by the mask from the other channel/type. Setting this parameter to 'negative' allows the user to exclude voxels that have an ambiguous identity. The parameter 'selection_type' allows the user to choose the threshold method for labeling a colocalization as either axonal or dendritic. The user can set this to 'pure', in which an aggregate can only overlap with one of the mask types, 'any' in which any amount of over overlap counts for colocalization, or 'percent' in which aggregates are classified as axonal or dendritic based upon which is most prominent. 'Percent' is recommended as it allows for high confidence in the indentified type without being overly resistrictive such as when using 'pure'.
The user can define a naming suffix to aid in labeling output images.
Due to post-acquisition Airyscan Processing, the user must define the size of the raw acquired image in order to correctly adjust the mosaic tiling key.
Due to loss of optical sections during the correction of chromatic aberration, the user must provide the number of Z-slices that remain in the images after correction.
The user just needs to run the next block of code to load required processing definitions and build the mosaic tiling key.
If the user has not processed a given image previously, they must run the next block of code. If the user has already performed the initial image processing and saved that output, this block can be skipped.
If the user is skipping the previous step and loading previously saved preprocessed images, this block can be used to reload that data.
Once the user has performed the previous image preprocessing steps, the next block can be run in order to perform the colocalization analysis. The only settings the user needs to define are the optical sections to be used for generating the output maximum intensity projections.
Once the colocalization analysis has been performed, the user can run the next block to organize the data for manual export. The user can obtain the number of aggregates localized to either an axon or dendrite in the variable 'all_organized_data_by_tile'. In that data table, column 2 provides the number of aggregates colocalized with a dendrite and column 3 provides the number colocalized with an axon. Each row is the data from an individual imaging tile.
If the user wants to perform randomization analysis, to model the colocalization results under conditions of random chance, the next block of code can be run.